Keywords: Neural Signal Processing - Time frequency analysis, Brain physiology and modeling, Neurological disorders - Epilepsy
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Keywords: Sensory Neuroprostheses - Visual, Neural Interfaces - Neural stimulation, Neural Interfaces - Neuroimaging
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Keywords: Neural Interfaces - Neural microsystems and Interface engineering, Neural Interfaces - Microelectrode and fabrication technologies, Neural Interfaces - Neural stimulation
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Keywords: Brain-Computer/Machine Interface - Robotics applications, Human Performance - Gait, Brain Functional Imaging - Classification, spatiotemporal dynamics
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Keywords: Neural signal processing, Brain-computer/machine Interface, Neural Interfaces - Computational modeling and simulation
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Keywords: Brain Functional Imaging - Classification, spatiotemporal dynamics, Neural Interfaces - Neuroimaging, Brain Functional Imaging - Multimodal
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Keywords: Brain Stimulation-Deep brain stimulation, Neurological disorders, Neural signal processing
Abstract: Deep brain stimulation (DBS) is a common therapy for the treatment of essential tremor (ET). Currently, this technology continuously delivers stimulation to deep brain regions to mitigate symptoms. Closed-loop DBS aims to deliver stimulation only when symptoms are present, thus improving battery life and decreasing potential side effects. In this study, we used an investigational DBS device implanted with an electrocorticography strip in a subject with essential tremor. Using local field potentials sensed from motor cortex, we built a system of classifiers capable of detecting tremor-inducing movement. These classifiers were incorporated into a closed-loop DBS system which changed stimulation voltage in real time to ameliorate tremor. This is the first time that machine learning has been used in a CLDBS system to detect symptoms and change DBS parameters in real time.

Keywords: Brain-computer/machine Interface, Neural signal processing, Brain Functional Imaging - EEG and Evoked Potentials
Abstract: The expansion of brain-computer interfaces (BCIs) to outside the research laboratory has historically been hampered by their difficulty of use. Well-functioning BCIs often require many channels, which can be difficult to properly prepare and require expert support. Low-channel setups, however, can lead to poor or unreliable classification of intent. Here we introduce a novel method for extracting more information from a single EEG channel and test it on a ten subject motor imagery dataset. Instead of looking at bandpower or phase synchrony, we test the average frequency within each trial to see if there are task-dependent changes in the spectral locations of neural frequency peaks. We show that using this feature in combination with standard bandpower features is significantly better than bandpower features alone across subjects, both for standard electrodes and electrodes that include a Laplacian filter.

Keywords: Motor Neuroprostheses - Prostheses, Neuromuscular Systems - Computational modeling and simulation, Neuromuscular Systems - EMG models, processing and applications
Abstract: Recently, we proposed a musculoskeletal model to simultaneously predict motion along metacarpophalangeal (MCP) and wrist flexion/extension degrees-of-freedom (DOFs) from surface electromyography (EMG) signals. Since wrist pronation/supination is also functionally important, we extended the musculoskeletal model to simultaneously estimate wrist pronation/supination in addition to wrist and MCP flexion/extension from surface EMG signals of six corresponding muscles. Kinematic data and surface EMG signals were acquired synchronously from an able-bodied subject. The subject was instructed to perform single-DOF movements at fixed or variable speed and simultaneous 3-DOF movements at variable speed during the experiment. The model included six Hill-type actuators, each with a contractile element and a parallel elastic element. Seven parameters were optimized for each of the six muscles. The average Pearson’s correlation coefficient (r) between measured and estimated joint angles across all trials was 0.91, indicating high positive correlation. The results demonstrated that the proposed model could feasibly simultaneously estimate 3-DOF joint angles during either independent-DOF or simultaneous 3-DOF movements from EMG signals. Our results promote the potential of the EMG-driven musculoskeletal model for clinical applications, such as prosthesis control.

Keywords: Motor learning, neural control, and neuromuscular systems, Neural signal processing
Abstract: Recently, some studies found that some neurons in dorsal premotor (PMd) show high selectivity during the planning of specific grasp movements. But it is still unclear whether the distinct discharging of these neurons during movement plans is related to external visual stimuli induced by the shape and size of the object or internal movement selection for preshaping fingers. Therefore, the aim of our study is to directly compare neuronal activity of PMd during the planning of internally and externally driven grasp movements. In this study, we designed an experiment that training the monkey use two gestures to grasp one object to look for the neurons which focus on the hand shapes planning in PMd. Neural signals were collected by microelectrode array when the monkey was performing the task. With single neural activity (SUA) analysis, we found that 18% of neurons in PMd were selective to gestures in planning period, which confirmed that some neurons in PMd are related to planning. Furthermore, the gesture classification accuracy analysis illustrated that the decoding result of these tuning neurons is reaching 93.5% in planning period. These results infers that PMd encodes internally driven grasp movement planning.

Keywords: Brain-computer/machine Interface, Brain Functional Imaging - EEG and Evoked Potentials, Human Performance - Sensory-motor
Abstract: In most Brain-Computer Interfaces (BCI) experimental paradigms based on Motor Imageries (MI), subjects perform continuous motor imagery (CMI), i.e. a repetitive and prolonged intention of movement, for a few seconds. To improve efficiency such as detecting faster a motor imagery, the purpose of this study is to show the difference between a discrete motor imagery (DMI), i.e. a single short MI, and a CMI. The results of experiment involving 13 healthy subjects suggest that a DMI generates a robust post-MI event-related synchronization (ERS). Moreover event-related desynchronization (ERD) produced by DMI seems less variable in certain cases compared to a CMI.

Keywords: Brain-computer/machine Interface, Human Performance - Ergonomics and human factors
Abstract: Disabled people often have difficulties in conveying their intentions to assistive vehicles, which can transport them to desired destinations, and a P300 brain-computer interface (BCI) system may help them use intelligent assistive vehicles by selecting a desired destination from predefined ones. However, real world driving often exposes the P300 BCI system to various illumination and noise environments and their effects on the performance of the system remain a question. The goal of this paper was to investigate this question by examining the effects of three levels of illumination and two levels of background noise on the accuracy of the system and user preference rating. Experimental results from twelve participants show that illumination does not have a significant effect on accuracy, although users prefer the low illumination level to others. However, noise significantly affects the system accuracy and user preference rating, with lower noise leading to higher accuracy and user preference rating. The study can not only provide new insights into the application of the P300 BCI for assistive vehicles in practice, but also facilitate the research on the effects of environmental factors on the performance of a P300 BCI system.

Keywords: Brain-computer/machine Interface, Neural signal processing, Neural Interfaces - Computational modeling and simulation
Abstract: Multi-target stimulus coding plays an important role in a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI). In conventional SSVEP-based BCIs, a large interval between two neighboring stimulus frequencies is often used to improve classification accuracy. Although recent progresses in stimulus coding and target identification methods that have significantly improved the accuracy even with a high-frequency resolution (e.g., 0.2 Hz), the effects of frequency resolution on classification performance have not been systematically and statistically explored. This study compared the classification accuracy of SSVEPs with five different frequency resolutions (0.2 Hz, 0.4 Hz, 0.6 Hz, 0.8 Hz, and 1.0 Hz) using three (one unsupervised and two supervised) target identification methods. Eight-class SSVEP data were extracted from a 40-class SSVEP dataset for each condition according to the five frequency resolutions. The results showed no significant difference between frequency resolutions when combining joint frequency-phase modulation (JFPM) coding and template-based target identification methods. The results suggested that the number of commands (i.e., visual stimuli) in an SSVEP-based BCI could be increased without compromising the information transfer rate of the BCI.

Keywords: Sensory Neuroprostheses - Visual, Neural Interfaces - Neural stimulation, Neural Interfaces - Neuroimaging
Abstract: Retinal prosthetic implants have shown potential to restore partial vision to patients blinded by retinitis pigmentosa or age-related macular degeneration, via a camera-driven multielectrode array that electrically stimulates surviving retinal neurons. Commercial epi-retinal prostheses mostly use charge-balanced symmetric cathodic-first biphasic pulses to depolarize retinal ganglion cells (RGCs) and bipolar cells (BCs), resulting in the perception of light in blind patients. However, previous clinical study for patients with Argus II epiretinal implants reported most evoked percepts by single electrode were elongated and aligned with estimated axon path of retinal ganglion cells, suggesting the activation of axon bundles. In this project, based on an established genetically encoded calcium indicator (GECI), we performed in vitro calcium imaging for different stimulation paradigms, focusing primarily on short duration pulse that can avoid axonal stimulation and selective activate targeted RGC soma. The findings support the possibility to manipulate the responses of RGCs through varying the stimulation waveform, thus potentially forming more ideal shape perception with higher spatial resolution in future retinal prosthesis design.

Keywords: Brain Stimulation - Transcranial direct current Stimulation (tDCS), Brain-computer/machine Interface, Neurological disorders - Diagnostic and evaluation techniques
Abstract: Stroke can be defined as a sudden onset of neurological deficits caused by a focal injury to the central nervous system from a vascular cause. In ischemic stroke (~87% of all strokes) and transient ischemic attack (TIA), the blood vessel carrying blood to the brain is blocked causing deficit in the glucose supply – the main energy source. Here, neurovascular coupling (NVC) mechanism links neural activity with the corresponding blood flow that supplies glucose and oxygen for neuronal energy. Brain accounts for about 25% of total glucose consumption while being 2% of the total body weight. Therefore, a deficit in glucose supply can quickly change brain’s energy supply chain that can be transient (in TIA) or longer lasting (in stroke, vascular dementia). Here, implications of the failure of brain’s energy supply chain can be dysfunctional brain networks in cerebrovascular diseases. Using near-infrared spectroscopy (NIRS) in conjunction with electroencephalography (EEG), a non-invasive, real-time and point of care method to monitor the neuroenergetic status of the cortical gray matter is proposed. Furthermore, we propose that NIRS-EEG joint-imaging can be used to dose non-invasive brain stimulation (NIBS) – transcranial direct current stimulation (tDCS) and photobiomodulation – which may be able to provide therapeutic options for patients with energetic insufficiency by modulating the cortical neural activity and hemodynamics.

Keywords: Motor Neuroprostheses - Epidural Stimulation, Neural Interfaces - Computational modeling and simulation, Human Performance - Modelling and prediction
Abstract: Epidural spinal cord stimulation (SCS) is a promising therapy for spinal cord injury (SCI). This paper combines experimental data from epidurally-stimulated human paraplegic patients with computational models of SCS to identify the electric field features correlated with the patients' ability to stand. We locate the spinal cord regions most critical to stimulated standing and find that the most informative stimulating features agree with results from nerve fiber theory. Further applications of our work include developing algorithms to optimize stimulation configurations for SCI patients, determining optimal electrode placement, and considering novel electrode array designs.

Keywords: Brain Stimulation-Deep brain stimulation, Neurological disorders, Neural Interfaces - Computational modeling and simulation
Abstract: Deep brain stimulation (DBS) is an established therapy for a variety of neurological disorders, including Parkinson's disease, essential tremor, and dystonia. Recent DBS research has pursued methods for closed-loop control to provide more effective management of symptoms, side effects, and device power consumption. Most closed-loop DBS (CLDBS) studies to date use simple threshold-based controllers to trigger DBS and, as a result, any optimization of symptoms and device power consumption is only incident. In this paper, we demonstrate the utility of an approach based on identifying patient-specific models of symptom response to DBS and using these models to formulate a model predictive control strategy for CLDBS, which explicitly solves an optimization problem. We simulate the model predictive controller for various parameters and find that this approach yields a range of performances for the competing objectives of minimizing patient symptoms and device power consumption. We examine this fundamental tradeoff using the concept of Pareto optimality and conclude with a discussion about incorporating patient, clinician, and other stakeholder preferences in the design of CLDBS systems.

Keywords: Brain-computer/machine Interface, Human Performance - Cognition
Abstract: Visual brain-computer interfaces (BCIs) have achieved great progress in speed recently. But the problem of visual fatigue caused by intense flashes poses a great challenge in designing practical systems for long-term use. A direct way to improve visual comfort is to reduce the stimulus contrast. But it could also weaken the featured evoked potentials, which would bring a negative impact on system accuracy. Thus it’s significant to figure out the optimal contrast that could have both high visual comfort and high accuracy. This study investigated the effects of different stimulus contrasts on the two aspects. Six hybrid P300-SSVEP spellers were developed with different stimulus contrasts. Three subjects spelled 10 same characters offline for each speller. After each spelling subjects were asked to grade the flashes they just met in terms of visual comfort. Stepwise linear discriminant analysis (SWLDA) was used to recognize the P300 potential; the filter bank canonical correlation analysis (FBCCA) with individual template was adopted to classify the SSVEP. A decision fusion was performed to recognize the target. The results showed that, compared with P300 or SSVEP only features, the hybrid features significantly improved the accuracy. The subjects felt more comfortable for contrasts below 25%. The classification accuracy wouldn’t have a great loss unless the contrast was below 12%. Taken together, the optimal contrast was 12% for the hybrid P300-SSVEP BCI system in this study.

Keywords: Brain Stimulation - Transcranial Ultrasound Stimulation (TUS), Neural Signal Processing - Nonlinear analysis, Neural Interfaces - Neuroimaging
Abstract: The difference between the early stages of Parkinson's Disease (PD) and other diseases with similar symptoms is quite difficult to discern. Thus, hyperechogenicity of the Substantia Nigra (SN) revealed in ultrasound imaging has become a standard diagnostic marker for accurately diagnosing PD, as it is only common in PD patients. This has resulted in Transcranial B-mode Ultrasound Imaging (TCUI) becoming a widely used tactic for diagnosis of PD, as ultrasound is naturally well-suited to detect echogenicity. The accepted cutoff for hyperechogenicity is an echogenic area of 0.2,cm^2. Currently, clinician outline the echogenic area manually with a cursor, which naturally leaves room for ambiguity and human error. Unfortunately standard B-mode images of the SN are noisy enough that determining the boundaries of the echogenic area are typically quite ambiguous. This is why we suggest the use of the Third Order Volterra Filter (ToVF), which can separate an image into its linear, quadratic, and cubic components with no spectral overlap. One common method of implementing the Volterra filter is with an adaptive Least Mean Squares (LMS) algorithm. This paper examines Zero-Attracting variants of LMS algorithms, which take advantage of the sparse nature of ultrasound data for improved performance. We found that the Zero-Attracting algorithms converged to lower steady state errors, and also performed better in terms of dynamic range and boundary definition.

Keywords: Brain-computer/machine Interface, Neural signal processing, Brain Stimulation-Deep brain stimulation
Abstract: Current Brain Computer Interface (BCI) systems are limited by relying on neuronal spikes and decoding limited to kinematics only. For a BCI system to be practically useful, it should be able to decode brain information on a continuous basis with low latency. This study investigates if force can be decoded from local field potentials (LFP) recorded with deep brain electrodes located at the Subthalamic nucleus (STN) using data from 5 patients with Parkinson’s disease, on a continuous basis with low latency. A Wiener-Cascade (WC) model based decoder was proposed using both time-domain and frequency-domain features. The results suggest that high gamma band (300-500Hz) activity, in addition to the beta (13-30Hz) and gamma band (55-90Hz) activity is the most informative for force prediction but combining all features led to better decoding performance. Furthermore, LFP signals preceding the force output by up to 1256 milliseconds were found to be predictive of the force output.

Keywords: Brain-computer/machine Interface, Brain Physiology and Modeling - Neural circuits, Motor Neuroprostheses - Prostheses
Abstract: Stereo-electroencephalographic (SEEG) depth electrodes were used to record neural activity from deep brain structures in this study. By localizing all the electrodes into the individual brain, we found that areas that are inside of central sulcus occurred obvious hand-movement-related modulation when the subjects were performing different hand motion tasks. Then, an asynchronous brain-computer interface which enables the subject to control a prosthetic hand in real time was built. The testing results showed that, using the SEEG signals taken only from five channels, all the participants can not only control the robot prosthesis to be motionless but also can command the device to make three different hand gestures successfully with an average accuracy of 78.70±4.01%.

Keywords: Sensory Neuroprostheses - Auditory, Sensory Neuroprostheses, Neural Interfaces - Sensors and body Interfaces
Abstract: Localizing sounds in our environment is a fundamental perceptual ability. However, methods to assess sound localization ability are often cumbersome, requiring large speaker array systems. In adults with sensorineural hearing loss who are fitted with cochlear implant (CIs), sound localization is known to improve when bilateral CIs are used compared to a single CI. This study proposes a portable virtual reality (VR) tool for measuring sound localization in bilateral cochlear implant (CI) users. Head related transfer functions (HRTFs) were used to replicate 3-dimensional sounds delivered to the users via headphones or a direct connect (DC) CI audio cable. Users wearing the VR headset were then instructed to turn and face these sound sources. From measurements of head position and velocity, objective measures of accuracy were derived. Pilot testing of the device was conducted with 4 bilateral CI participants and 12 normal hearing (NH) participants. Results showed that NH listeners performed better than CI users in all metrics. Bilateral CI users performed above chance level when presented with sounds to their first implanted ear only or to both implants simultaneously. Compared to previously reported localization studies, NH listeners demonstrated lower performance using this VR system. This study provides a foundation for objective sound localization testing in bilateral CI users in a clinical setting.

Keywords: Brain-computer/machine Interface
Abstract: In P300 speller brain-computer interface (BCI), the stimulus sequence is presented to subject for several rounds to achieve reliable P300 detection. Traditionally, the number of rounds is fixed and relatively large (e.g., 15 in the Wadsworth Dataset of BCI Competition 2005), which results in low information transfer rate. In order to improve the speed of character recognition without affecting the spelling accuracy, we propose to use convolutional neural network (CNN) into the dynamic stopping. Compared with the traditional static stopping criterion (SSC), our method can effectively improve the information transfer rate of the system.

Keywords: Brain-computer/machine Interface, Neurorehabilitation - Virtual reality
Abstract: In this paper, a novel electroencephalographic (EEG) based mind controlled virtual-human obstacle-avoidance platform (EEG-MC-VHOAP) is designed to improve brain computer interface (BCI) systems and offer a new game. With the EEG-MC-VHOAP, subjects can use their brain signals to control a virtual human to have a training of avoiding obstacles in a three dimensional (3D) environment. The EEG-MC-VHOAP is composed of an EEG based BCI subsystem and a 3D virtual-human subsystem. In the EEG-based BCI subsystem, a self-adaptive bayesian linear discriminant analysis(SA-BLDA) is adopted to classify the P300 signals, and is then transformed into four control commands. The control commands are used to control the virtual-human to walk forward, walk in a crouch, turn left and turn right. Three subjects were asked to attend the testing with the EEG-MC-VHOAP. All subjects accessed to a 100% train accuracy for repeating flashing 4-6 trails, a less than 10% average collision rate, and a higher than 80% online accuracy. Both the training and online testing results demonstrate the effectiveness of the proposed EEG-MC-VHOAP.

Keywords: Sensory Neuroprostheses, Neural Interfaces - Recording, Neural Interfaces - Implantable systems
Abstract: The addition of sensory feedback is expected to greatly enhance the performance of motor neuroprostheses. In the case of stroke or spinal cord injured patients, sensory information can be obtained from electroneurographic signals recorded from intact nerves in the non-functioning limb. Here, we aimed to identify sensory information recorded from mixed nerves using a multi-channel cuff electrode. ENG afferent signals were recorded in response to mechanical stimulation of the foot corresponding to three different functional types of sensory stimuli, namely: nociception, proprioception and touch. Offline digital signal processing was used to extract features for use as inputs for classification. A quadratic support vector machine was used to classify the data and the five-fold cross validation error was measured. The results show that classification of nociceptive and proprioceptive stimuli is feasible, with cross-validation errors of less than 10%. However, further work is needed to determine whether the touch information can be extracted more reliably from these recordings.

Keywords: Motor Neuroprostheses - Prostheses, Neural signal processing
Abstract: Linear Discriminant Analysis (LDA) is the most commonly used classification method for movement intention decoding from myoelectric signals. In this work, we review the performance of various discriminant analysis variants on the task of hand motion classification. We demonstrate that optimal classification performance is achieved with regularized discriminant analysis (RDA), a method which generalizes various class-conditional Gaussian classifiers, including LDA, quadratic discriminant analysis (QDA), and Gaussian Naive Bayes (GNB). The RDA method offers a continuum between these models via tuning two hyper-parameters which control the amount of regularization applied to the estimated covariance matrices. In this study, we performed a systematic classification performance comparison on four datasets. Hand motion was decoded from myoelectric and inertial data recorded from 60 able-bodied and 12 amputee subjects whilst they performed a range of 40 movements. We found that when the regularization parameters of the RDA classifier were carefully tuned via cross-validation, classification accuracy was statistically higher by a large margin as compared to any other discriminant analysis method (average improvement of 13.7% over LDA). Importantly, our findings were consistent across the able-bodied and amputee populations. This observation provides supporting evidence that our proposed methodology could improve the performance of pattern recognition-based myoelectric prostheses.

Keywords: Brain-computer/machine Interface, Brain Functional Imaging - EEG and Evoked Potentials, Clinical neurophysiology
Abstract: Visual fixation is an item of the Coma Recovery Scale-Revised (CRS-R), it is difficult to be detected by clinicians using the behavioral scales because of fluctuations of arousal level and the presence of motor impairment in disorders of consciousness (DOC) patients. Brain-computer interfaces (BCIs), which directly detect brain response without any behavioral expression, can be used to evaluate a patient's response to the stimuli. In this study, we explored the application of a 4-choice visual BCI in assisting visual fixation assessment of DOC patients. The results obtained from three patients indicated that one locked-in syndrome (LIS) patient showed visual fixation with the BCI assessment while exhibit higher visual function with the CRS-R assessment. The other two patients did not show any response to visual fixation with BCI assessment. The result revealed that EEG based BCI could detect brain response to the evaluation item of CRS-R in DOC patients. Therefore, the proposed BCI may provide a promising method for more objective result and thus assist CRS-R behavioral assessment.

Keywords: Brain-computer/machine Interface, Brain Functional Imaging - fMRI, Neurorehabilitation - Virtual reality
Abstract: The ability to allow subjects, including paralyzed patients, to perform a task using brain-computer interfaces has seen a rapid and growing success. Surprisingly, however, it is still not known how far such performance can be improved - especially in cases of long term amputation where both efferent and afferent functions are abolished and may lead to deterioration of the relevant brain representations. Here we used real-time fMRI to demonstrate a remarkably high performance of long term amputees in controlling a computer generated avatar using their missing hand. The missing limb BCI performance showed similar levels both when compared to the intact hand and to control participants.

Keywords: Brain Stimulation - Transcanial magnetic stimulation (TMS)
Abstract: Transcranial Magnetic Stimulations (TMS) have been applied to medical cares of brain dysfunctions. However, in some cases, conventional figure-8 coils which induce the magnetic field have some problems, such as the excessive localized stiumulation area. In this study, we propose new wide focus coils named double-D coils. The radius of side elements and the number of coil turns were defined with simulations based on scalar potential finite difference method. The distribution and intensity of the electric field induced by the double-D coil were simulated based on finite element method. We also conducted the simulations using brain models constructed from MR images to evaluate its robustness against the coil positioning error. Finally, we developed the double-D coil by using a 3D printer and measured the magnetic flux density. The results showed the double-D coil induced an electric field in an expanded area. This was enough to be applied to a smaller positioning system and the intensity of the electric field was improved at more than 16 mm from the coil surface in the simulation. In the brain model from the MR image, the mean value of the electric field by the double-D coil was 1.2 times stronger than that of the figure-8 coil. The double-D coil created by a 3D printer induced a similar magnetic field to the simulation. That indicates the efficiency of the double-D coil at more than 20 mm from the coil surface.

Keywords: Brain-computer/machine Interface, Brain-Computer/Machine Interface - Robotics applications
Abstract: Brain-machine interface (BMI) can be used to control robotic arm to assist paralysis people improving their quality of life. However process control of objects grasping is still a complex task for BMI users. High efficiency and accuracy is hard to achieve in objects grasping process even after extensive training. An important reason is lack of sufficient feedback information for performing the closed-loop control. In this study, we describe a method of augmented reality (AR) guiding assistance to provide extra feedback information to the user for closed-loop control. A hybrid BMI based system with AR feedback is proposed to evaluate the performance of our method in objects grasping task using robotic arm. Reaching and releasing tasks are completed by the robotic arm automatically. For the grasping task controlled by the user, AR is used to enrich the normal visual information during the grasping process to provide the BMI user augmented feedback information about the gripper status in real time. The feasibility of the proposed system both in open-loop (visual inspection) and closed-loop (AR feedback) are compared. According to our experimental results obtained from 5 subjects, the time used for controlling the robotic arm to grasp objects with AR feedback reduces more than 5s and the error rate of the gripper aperture decreases approximately 20% compared to those of grasping with normal visual inspection only. The results reveal that the BMI user can benefit from the informat

Keywords: Brain-computer/machine Interface, Brain Functional Imaging - EEG and Evoked Potentials, Human performance
Abstract: This paper presents a hybrid collaborative brain-computer interface (cBCI) to improve group-based recognition of target faces in crowded scenes recorded from surveillance cameras. The cBCI uses a combination of neural features extracted from EEG and response times to estimate the decision confidence of the users. Group decisions are then obtained by weighing individual responses according to these confidence estimates. Results obtained with 10 participants indicate that the proposed cBCI improves decision errors by up to 7% over traditional group decisions based on majority. Moreover, the confidence estimates obtained by the cBCI are more accurate and robust than the confidence reported by the participants after each decision. These results show that cBCIs can be an effective means of human augmentation in realistic scenarios.

Keywords: Brain-computer/machine Interface, Neurological disorders - Diagnostic and evaluation techniques, Brain Functional Imaging - EEG and Evoked Potentials
Abstract: The aim of this study is to determine the potential prognostic value of using Brain-computer Interface (BCI) to identify patients with disorder of consciousness (DOC), who show potential for recovery. A retrospective study involved 51 patients with DOC were conducted. Each patient conducted in a BCI experiment to detect awareness and received a 3-months follow-up. The BCI accuracies were correlated to patient outcomes according to the Coma Recovery Scale Revised (CRS-R). The statistical tests showed that patients with significant accuracies higher than the chance level showed greater improvement in CRS-R scores after 3 months compared to patients without significant accuracies. The sensitivity and specificity of the BCI accuracies in determining individual's consciousness recovery after 3 months are 67.7% and 90% respectively. A highly significant relationship between BCI accuracies and subsequent recovery was thus found. The BCI is suggested as an important tool to assess information-processing capacities that can predict the likelihood of recovery in patients with disorder of consciousness.

Keywords: Brain Stimulation-Deep brain stimulation, Neural Interfaces - Neural stimulation, Neural Interfaces - Microelectrode and fabrication technologies
Abstract: Delivery and control of untethered neuronal stimulation devices deep in the brain is currently difficult to perform and control with cell-level spatial resolution, but would be useful in both research and clinical applications. Magnetic particles can be noninvasively delivered with high precision to deep structures through dynamic magnetic inversion. This article demonstrates in an invertebrate animal that neuronal stimulation can be achieved using mechanical vibration of implanted particles, in which the vibration is realized through an externally-applied magnetic field under MRI guidance. Potential eventual applications of the technology include stimulation and modulation of the deep brain or peripheral neurons, using wearable electromagnetic coils for control and activation.

Keywords: Brain Stimulation - Transcranial direct current Stimulation (tDCS), Neuromuscular Systems - Neurorehabilitation, Neurological disorders - Stroke
Abstract: This study sought to investigate two-electrode montages for the application of anodal transcranial direct current stimulation (atDCS) over ipsilateral cerebellar hemisphere during visuomotor learning of myoelectric visual pursuit using electromyogram (EMG) from gastrocnemius (GAS) muscle. The atDCS montages were selected based on computational modeling to target electric field strength at the anterior lobe (AL) or posterior lobe (PL) or AL+PL of the cerebellum. AL atDCS resulted in a statistically significant (p<0.05) decrease in RT post-intervention than baseline when compared to PL atDCS and AL+PL atDCS. However, only AL+PL atDCS resulted in a significant decrease in RMSE post-intervention than baseline. Here, optimizing the direction of the electric field relative to cerebellar peduncles may be relevant.

Keywords: Motor neuroprostheses, Neuromuscular Systems - EMG models, processing and applications, Neural Signal Processing - Nonlinear analysis
Abstract: This paper presents a polynomial ridge regression algorithm with substantial improvements in computational efficiency compared with the polynomial kernel ridge regression and the standard polynomial regression. This regression algorithm was combined with a Kalman Filter (KF) to yield the Directly Weighted Polynomial Ridge Regression KF (DWPRR-KF). Experiments conducted offline from data collected from a human amputee demonstrated that compared with a linear KF, the DWPRR-KF significantly reduced median range-normalized Root Mean Square Error (RMSE) caused by movement on Degrees Of Freedom (DOFs) that the user intended to hold stationary during movement of other DOFs by 63% (from 0.061 to 0.023), while insignificantly increasing median error on DOFs the user intended to move (4%; from 0.138 to 0.144). Furthermore, the median overall error, from DOFs with or without intended movement, decreased by 27% (from 0.085 to 0.063) but this change was not found significant.

Keywords: Brain-computer/machine Interface, Brain Functional Imaging - EEG and Evoked Potentials, Human Performance - Sensory-motor
Abstract: Kinesthetic motor imagery (KMI) tasks induce brain oscillations over specific regions of the primary motor cortex within the contralateral hemisphere of the body part used in the process. This activity can be measured through the analysis of electroencephalographic (EEG) recordings and is particularly interesting for Brain-Computer Interface (BCI) applications. The most common approach for classification consists in analyzing the signal during the course of the motor task within a frequency range including the alpha band, which attempts to detect the Event-Related Desynchronization (ERD) characteristic of the physiological phenomenon. However, especially to discriminate right-hand KMI and left-hand KMI, this scheme leads to poor results on subjects for which the lateralization is not significant enough. To solve this problem, we propose to analyze the signal at the end of the motor imagery within a higher frequency range, which contains the Event-Related Synchronization (ERS). This work presents that 6 over 15 subjects have a higher classification rate after the KMI than during the KMI, due to a higher lateralization during this period. Thus, for this population we can obtain a significant improvement of 13% in classification taking into account the user's lateralization profile.

Keywords: Brain-computer/machine Interface, Motor Neuroprostheses - Prostheses, Neural signal processing
Abstract: Grasping objects is one of the most important hand utilisation in everyday life. Due to neuromuscular ailments or injury, some people are unable to move their hands. Though myoelectrically controlled prostheses are widely available in the market, they require some muscle based control points which are hardly available for many. Motor Imagination (MI) controlled prostheses will surpass this shortcomings for them. However, restoring human grasping from MI is a challenging task. In this study, we have decoded two major types (Power and Precision) of grasping along with their three subtypes each from motor imagery. A comparative study of various time domain analysis combined with different classifiers is presented here to find an optimum choice of feature and corresponding classifier. It has been concluded that Hjorth parameters classified with kNN classifier have yielded the highest accuracy of 97.9% while separating different types of grasping from motor imagination.

Keywords: Sensory Neuroprostheses - Visual, Neural Interfaces - Neural stimulation, Neural Interfaces - Implantable systems
Abstract: Retinal degenerative disorders are one of the leading causes of human blindness in adult life. Electronic retinal prostheses aim to restore vision in blind people who have photoreceptor cell loss by electrically stimulating the inner retina. We conducted in-vivo experiments, in which we electrically stimulated the retina and measured electrically evoked potentials (EERs) from the visual cortex of rat brains. We mapped the regions of electrophysiology activity in the visual cortex in response to electrical stimulation of the retina. Cortical activity was recorded in the same regions as seen with light stimulus of retina in previously published studies. The strength of the electrically evoked responses in the visual cortex showed a dose-response characteristic with respect to the amount of charge delivered to the retina.

Keywords: Motor Neuroprostheses - Prostheses, Neural signal processing, Neural Interfaces - Implantable systems
Abstract: This paper investigates whether the movement intent of an individual with amputation can be classified in real-time as the individual moving his/her phantom hand. We present a method to detect movements intent using neural signals from the peripheral nervous system (PNS). In addition, we classify eight types of individual hand movements using 300 ms signal segments beginning with our detected starting time. Classification is performed by applying linear discriminant analysis (LDA) on different features. We compared the classification results using segments determined by the detected starting time and the starting time of the command given to a subject as neural signals were recorded. The average accuracies were 73.5% in the former case and 59.4% in the latter. Although we performed these analyses offline, our approach has a potential for real-time use as the detection and classification are based on 300 ms segments of the neural signal. Future work includes neural signal decoding based on the classification. The work presented in the paper and the follow up work will form the basis for a complete neuro-prosthesis decoding system that can detect and decipher the amputee’s movement intent in real-time and control the prosthesis in an efficient and naturalistic manner.

Keywords: Motor Neuroprostheses - Epidural Stimulation, Neuromuscular Systems - Neurorehabilitation
Abstract: Both treadmill training and epidural stimulation can help to reactivate the central pattern generator (CPG) in the spinal cord after a spinal cord injury. However, designing an appropriate training approach and a stimulation profile is still a controversial issue. Bearing the Hebbian theory in mind, one can speculate a relation between the number of input afferent signals and the quality of movement recovery. In this paper, this conjecture was confirmed through some simulation studies on a model of CPGs in which the influence of increasing the afferent input weight on activating the CPG model was verified. Moreover, the performance of two different types of treadmill training along with epidural stimulation was assessed. The experimental results coincide with the achieved simulation results elucidating the effect of increasing the afferent input weights, means increasing the number of involved spinal sensory feedback during the training, on expedition of spinal CPG reactivation.

Keywords: Brain Stimulation - Optogenetics, Neural Interfaces - Neural stimulation, Neural Interfaces - Implantable systems
Abstract: Animal studies are often used to test the feasibility and effectiveness of neuroscience research ideas. Optogenetics is a state-of-the-art technique that allows researchers to control brain activity with light. Current methods are limited as they use tethered setups with the animal in a fixed position, resulting in stress and reduced animal welfare. Hence, an untethered setup is highly desirable.

We propose a battery-less, wireless optogenetic stimulation setup based on resonant inductive coupling, allowing for full freedom of movement of multiple rodents in a 40×40×20 cm environment. Our design includes: a transmitter coil capable of powering the optogenetic stimulation receiver regardless of lateral and vertical misalignments; a 1×1×1 cm light-weight head-mounted receiver module with the receiver coil, rectifying and regulating electronics, and a microcontroller; and creation of both rigid and fully-flexible, cost-effective optogenetic optrodes using a novel µLED mounting technique allowing multiple µLEDs to be directly inserted into the brain.

The setup offers a novel and robust solution for freely moving animal studies. The inductive link has a maximum link efficiency of 0.56% at the maximum coupling factor of 0.31%. For an input current of 0.5 A into the primary coil, even for half the peak link efficiency, and an angular misalignment of 45 degrees, the setup can deliver 8.5 mW of light power into the brain.

Keywords: Sensory Neuroprostheses - Visual, Brain Stimulation - Sensory restoration, Neurorehabilitation
Abstract: Visual prosthesis opens new perspectives in the field of restoring vision for blind people. It aims to bypass the defective stages of the natural visual pathway and to provide proper inputs to the later stages. The starting point of any visual prosthesis is an image processing model which is typically performed in two phases: Feature extraction to highlight the important information of visual scene, followed by a visual pathway stimulation which replaces the functionality of the defective stages of the visual pathway. This paper introduces a MATLAB toolbox for visual prostheses to handle all the required image processing. This toolbox supports the most common image processing techniques needed for both static and dynamic scenes recognition. In addition, it supports different kinds of visual prostheses like retinal and thalamic visual prostheses depending on how deep the visual stimulation phase will go. The paper demonstrates the value of the toolbox through a case study in which it was used for a simulated thalamic visual prosthesis.

Keywords: Motor Neuroprostheses - Neuromuscular stimulation, Motor Neuroprostheses - Robotics, Neurorehabilitation - Robotics
Abstract: Abstract--- A hybrid neuroprosthesis is a device that uses a combination of electric motors and functional electrical stimulation (FES) to provide gait assistance. Its closed-loop control performance can be potentially affected by the presence of electromechanical delay (EMD) during FES. In this paper, a tracking control scheme for a hybrid walking neuroprosthesis that combines electric motor actuation at the hip and FES actuation at the knee is presented. The knee joint controller uses a delay compensation technique to compensate for EMD during FES. This neuroprosthesis controller is combined within a finite state machine that also features gait detection, wherein force sensors in the foot can detect gait phases and create a fully automated and functional assisted gait cycle. Experiments were performed on an able bodied subject to demonstrate the efficacy of the tracking control scheme. Results from the experiments show a maximum error at the hip of less than 1 degree and a maximum error at the knee of 13.66 degrees. The maximum error at the knee is attributed to overshoot caused by the unidirectional actuation of the FES.

Keywords: Brain Stimulation - Optogenetics, Brain physiology and modeling - Neuron modeling and simulation, Neurological disorders - Epilepsy
Abstract: This paper presents a distributed open-loop optogenetic control for suppression of epileptiform activity in a neural population model of cortex. In epilepsy, cortical seizures or epileptiform activities occur when pyramidal cells become hyper-excitable due to the loss of inhibitory interneurons. A straightforward way to suppress these epileptiform activities is to inhibit pyramidal cells by exciting interneurons. Thus, in this paper, the inhibitory neural population is targeted for the application of open-loop optogenetic control. By introducing computational model of the light-gated Channelrhodopsin-2 (ChR2) ion channels into the well-known Wilson-Cowan model, we first establish a neural population model for optogenetic control of cortical dynamics. Then, we investigate the effects of open-loop optogenetic control parameters (irradiance intensity and pulse duration) on the control performance. Finally, we use a spatially distributed control strategy to normalize cortical dynamics with minimum optical stimulations. The simulation results demonstrate the effectiveness of our propose control method for suppression of epileptiform activities.

Keywords: Neurorehabilitation - Virtual reality
Abstract: Virtual reality is an interactive synthetic environment generated by a computer and is commonly regarded as a means of sensory stimulation in neural engineering. A series of physiological and psychological reactions will be generated while immersing in virtual reality. In order to verify gender differences in vection perception and potential physiological responses, 55 articipants (28 females) were enrolled in an immersive virtual reality experiment simulating the optokinetic drum to invest the gender effect on vection perception and postural responses. We found vection perception was of no gender differences as well as postural oscillation onset. But postural responses presented stronger in women. The results might support the multi-sensory information explanation of gender difference in performing a directional task.

Keywords: Brain-computer/machine Interface, Brain Functional Imaging - Multimodal, Neural Interfaces - Neuroimaging
Abstract: We attempted to increase the bit rate (R) by combining electroencephalography (EEG) and near-infrared spectroscopy (NIRS) for eyes-closed (EC) brain-computer interface (BCI). We could achieve an average classification accuracy of 86.7 % and R of 0.86 bits/min, which outperformed that of the conventional NIRS EC BCI and successfully verify the availability of the hybrid EEG-NIRS EC BCI.

Keywords: Brain-computer/machine Interface, Brain Functional Imaging - EEG and Evoked Potentials, Neural signal processing
Abstract: The aim of the present study was to investigate whether it is possible to continuously track changes of individual mental stress over a long period of time with a wireless EEG device. Eleven healthy subjects participated in the experiments for 10 weeks. We found EEG indices showing strong correlations with temporal changes of self-scoring stress questionnaire scales.

Keywords: Neurorehabilitation, Neurological disorders - Stroke, Motor Neuroprostheses - Epidural Stimulation
Abstract: Low frequency quasi-oscillatory activity is present in rat motor cortex during reach-to-grasp, and closely tracks motor performance before and after stroke. Brief on-demand epidural direct current stimulation (DCS) applied during movement modulated low frequency local field potential (LFP) and improved post-stroke performance.

Keywords: Brain-computer/machine Interface, Neural signal processing, Motor neuroprostheses
Abstract: We present data towards the question of whether spike sorting is beneficial for decoding in motor-cortical brain-machine interfaces. We note that some past studies on this question were confounded by the presence of hash waveforms, threshold-crossings which do not match any unit’s template or criteria. We and others have found that including the count of hash waveforms (as another multiunit) improves decoding accuracy. When the comparison of sorted vs unsorted is controlled for the presence of hash, we found that sorted is consistently better than unsorted for decoding accuracy.

Keywords: Motor neuroprostheses, Sensory Neuroprostheses - Somatosensory and vestibular, Brain Stimulation - Optogenetics
Abstract: In this project, we investigate the impact of somatosensory feedback on learning a motor task in a Brain Machine Interface (BMI). Here, we report the successful integration of electrophysiology and patterned optogenetics in a closed-loop BMI, yielding an a round-trip duration of median 27 ms.

Keywords: Brain-computer/machine Interface, Motor Neuroprostheses - Prostheses, Neuromuscular Systems - Neurorehabilitation
Abstract: Brain-computer interfaces (BCIs) aim to help people with impaired movement ability by translating their movement intentions into control signals for assistive technologies. Despite substantial performance improvements in recent years, BCIs still make errors that need to be corrected by the user. Here we show that such errors in a trial’s outcome (i.e., selecting an undesired key on a virtual keyboard) can be decoded from the human primary motor cortex (M1) and can potentially be used to improve BCI performance by automatically detecting and undoing erroneous actions.

Keywords: Brain-computer/machine Interface, Brain Functional Imaging - NIR, Brain-Computer/Machine Interface - Robotics applications
Abstract: In this paper, the use of initial dips for the reduction in command generation time using functional near-infrared spectroscopy (fNIRS) for brain-computer interface (BCI) is investigated. fNIRS signals of five subjects are obtained during mental arithmetic (MA) and mental counting (MC) tasks from the prefrontal cortex. The initial dips are detected using vector-phase analysis approach. For the initial dips classification, signal mean and signal peak of oxy-hemoglobin (HbO) in four different window sizes (0~0.5, 0~1, 0~1.5, and 0~2 sec) are tested. The time period of 0~2 sec gives the best classification accuracy of 72% for two classes. The two-class classification using conventional hemodynamic response (HR) in which signal mean and signal slope in 2~7 sec have yielded the averaged classification accuracy of 84%. The results reveals that the fNIRS-based BCI using initial dips detection can reduce the command generation time to 2 sec.

Keywords: Neurorehabilitation, Neurorehabilitation - Neurofeedback, Neuromuscular Systems - Neurorehabilitation
Abstract: This study proposes a motor imagery amplification paradigm by using rubber hand illusion (RHI) for guidance of motor imagery. The experimental result shows that imaginary event-related desynchronization under the proposed paradigm is statistically amplified compared with that under MI (p<0.05).

Keywords: Brain-computer/machine Interface, Brain Functional Imaging - EEG and Evoked Potentials
Abstract: A real-time mobile phone dialing system using steady-state visual evoked potential(SSVEP) based brain computer interface(BCI) is presented in this paper. In this dialing system, a compact and portable device named OpenBCI is used to acquire electroencephalogram(EEG) signal instead of using NeuroScan system which is large and redundant. The obtained EEG signal is analyzed by canonical correlation analysis(CCA) algorithm to identify the specific numbers which the subject is dialing. The experiment results show that the OpenBCI device also has a high accuracy in the dialing system.

Keywords: Brain-computer/machine Interface
Abstract: For a practical intracranial brain computer interface (BCI), minimizing the invasiveness of the electrode implantation is crucial. In this study, we used only one intracranial electrode to implement an online BCI for fast typing. When the subject attended the virtual button containing visual motion stimuli, prominent responses were elicited at the stereo-EEG (SEEG) electrodes within the fMRI defined middle temporal (MT) region, which were composed of motion-onset visual evoked potential (mVEP) around 200 ms post-stimulus and a power increase at the high gamma (70-100 Hz) frequency range. In the online BCI experiment with surgical epilepsy patients, by combining both mVEP and high gamma features and using smart stopping strategy, single SEEG electrode supported a speed of BCI typing up to 14 characters per minute. Our findings demonstrate the feasibility of implementing a minimally invasive intracranial BCI with only one electrode for fast typing.

Keywords: Brain-computer/machine Interface, Neural signal processing
Abstract: We introduce a free and open access dataset for electroencephalography (EEG) - near-infrared spectroscopy (NIRS) hybrid brain-computer interface (BCI). The dataset was acquired from twenty-eight subjects who performed motor imagery (left vs. right-hand) and mental arithmetic (mental subtraction vs. baseline) tasks. The dataset was validated using basic signal analysis methods as a reference result, which the classification accuracy was examined for each modality and both modalities together. We expect that our dataset can be used in a wide range of future validation approaches in multimodal BCI research.

Keywords: Neural signal processing, Neural Signal Processing - Blind source separation
Abstract: Traditional measurement of somatosensory evoked potentials (SEP) depends on averaging of a lot of recordings, which makes a loss of dynamic variability. Single trial extraction provides a new measurement of SEP latency variability to evaluate neurodynamic status of somatosensory pathway. This study is to apply single trial based SEP to diagnose severity of demyelination changes in a chronical spinal cord injury model. The severity of demyelination was evaluated by histological examination with Luxol fast blue staining (LFB). Results showed that the latency variability based on single trial SEP were well correlated with the severity of demyelination measured by histology (r=-0.90 and r=-0.95). It suggested that single trial SEP can provide a dynamic measurement to be an indication of spinal cord demyelination.

Keywords: Neural signal processing, Neural Signal Processing - Time frequency analysis, Human Performance - Attention
Abstract: One major concern in todays post–surgical cochlear-implant (CI) fitting is to match the perceived loudness (PL), provided by the subjective feedback of the CI-user, to the objective intensity of a sound by regulating the transmitted charges of the single electrodes. This process is challenging and time consuming even with cooperative users, thus there is a major need for an objective evaluation of the PL. In this work we demonstrate a method to objectively evaluate the subjectively PL of CI-users by quantifying the habituation effect of the N1-P2-complex of the cortical auditory free–field evoked potentials using time-scale-representations.

Keywords: Human Performance - Sensory-motor, Human Performance - Cognition, Human performance
Abstract: Thermosensation represents a relatively unexplored modality for information transmission in communication interfaces. To explore the upper limits of discriminative ability, we asked if performance at warm temperatures (35-37C) could be enhanced by topical application of the cold-sensitizing compound menthol, to bring cold sensing channels within the range of warm channels. We found that although menthol enhanced subject’s confidence in their ability to finely discriminate phasic cooling-from-baseline pulses, actual performance was clearly impaired. This indicates that the brain cannot easily integrate cold and warm thermal afferent channels to improve discrimination, and that metacognitive and actual performance is dissociable.

Keywords: Motor neuroprostheses, Brain-computer/machine Interface, Motor Neuroprostheses - Robotics
Abstract: Brain-computer interfaces (BCIs) can provide a control signal source to allow people with movement impairments to control high degree-of-freedom (DOF) effectors such as prosthetic limbs. Improving these systems’ performance requires, in part, advances in high DOF neural decoding. Here we demonstrate that a person with tetraplegia can accurately, independently, and simultaneously control a three-DOF cursor, plus a binary ‘grasp’ command, using natural movement imagery decoded by an intracortical BCI.

Keywords: Brain-computer/machine Interface, Neural signal processing, Brain physiology and modeling - Neural dynamics and computation
Abstract: Falcon is an open-source general-purpose multi-threaded software platform for closed-loop neuroscience experiments. Falcon allows the user to implement highly customized processing pipelines with direct control over CPU resources. Here we demonstrate the soft real-time capabilities of Falcon in processing streaming multi-channel neural data.

Keywords: Neurorehabilitation, Neuromuscular Systems - Neurorehabilitation, Neurological disorders
Abstract: Stem cell therapy improves outcome after peripheral nerve injury, and human neural crest stem cells (hNCSCs) show exceptional promise. We evaluated the usability of hNCSC across cell culture passages for sciatic nerve regeneration by wet muscle weight and immunostaining. While hNCSCs remain potent by the fifth passage, there is a significant decrease in effectiveness by the sixth. This finding contributes significantly to future study design for stem cell therapy and related research.

Keywords: Brain Stimulation - Transcranial Ultrasound Stimulation (TUS), Neural Interfaces - Neural stimulation
Abstract: A novel, multi-purpose research platform is developed for in vitro and in vivo research of ultrasound (US) neuromodulation within the central nervous system (CNS) of rodents. The platform introduces high resolution, multifocal stimulation capabilities to this emerging field of study.

Keywords: Brain Stimulation - Optogenetics, Neural Interfaces - Microelectrode and fabrication technologies, Neural Interfaces - Neural stimulation
Abstract: Abstract—Optogenetic is a new neuromodulation method, which involves the use of light to excite or inhibit the specific neuronal activity through light-sensitive proteins. The traditional setup of optogenetics is to implant an optical fiber directly into the brain for light stimulation. In this study, we propose a novel multifunctional neural probe, which uses a specific fabrication technology to build an optical waveguide combined with a flexible neural probe. Thus, light stimulation and neural signal recording can be achieved simultaneously.

Keywords: Sensory Neuroprostheses - Visual, Neuromuscular Systems - Locomotion, posture and balance
Abstract: The purpose of this letter is to explore the role of vision, somatosensory system and vestibular in young adults’ postural balance. The result shows that the integration of vision , somatosensory and vestibular components are used to maintain one’s postural control, while vision is the predominant sensory system used by young adults.

Keywords: Brain-computer/machine Interface, Sensory Neuroprostheses - Somatosensory and vestibular, Brain functional imaging
Abstract: Stereoelectroencephalography (SEEG) steady-state somatosensory evoked potentials (SSSEP) were elicited applying vibrotactile stimulation (27Hz sine wave with 175Hz sine carrier wave) to each fingertip of an epilepsy subject’s contralateral hand. SSSEP were found spatially different among fingers. Especially, the 27Hz, 54Hz, 81Hz and 108Hz resonant like SSSEP were activated in specific location for a certain finger.

Keywords: Brain Stimulation - Transcranial direct current Stimulation (tDCS), Neurorehabilitation
Abstract: As one of the most well-known methods of neuromodulation, transcranial direct current stimulation (tDCS) is a promising therapeutic tool for Parkinson’s disease (PD). Extant literature has reported the technique to be able to affect both motor and non-motor symptoms of PD. However, different montages might affect Parkinsonian symptoms differently. In this research, we aimed to investigate the instant effect of a new montage termed bilateral tDCS, exclusively over hand dexterity of patients with PD. A randomized, double-blinded, sham-controlled experiment was conducted on 6 confirmed PD subjects (with hand tremor). A direct current of 1.5 mA was applied over the primary motor cortex (M1) of both hemispheres. The primary outcome measure chosen was a 3-trial Purdue Pegboard test (PPT). Opposed to the sham montage, results show that only bilateral tDCS could significantly increase PPT score to a large extent (p<0.05) and therefore hand dexterity of PD patients. Finally, we concluded that bilateral tDCS might become another promising montage for treatment of PD in the future. The rationale of the montage might lie in the rebalance of interhemispheric excitability.

Keywords: Brain-computer/machine Interface, Neural Interfaces - Implantable systems, Neural Interfaces - Neural microsystems and Interface engineering
Abstract: In clinical applications, brain–machine interfaces (BMIs) technology can help patients suffering from diseases such as amyotrophic lateral sclerosis, spinal cord injury, and paralysis because BMIs have the potential to enable us to control machines such as prosthetic arms or communication tools using only neuronal signals. Therefore, we have been developing a multichannel recording system which consists of custom designed 3-dimensional ECoG electrodes, neural recording ASICs, a wireless data transmitter, a wireless power receiver with a rechargeable battery. In this paper, we demonstrate the desirable high-gamma activity related to an actual left arm movement in monkey by wirelessly recorded ECoG signals.

Keywords: Brain-computer/machine Interface, Brain-Computer/Machine Interface - Robotics applications, Brain-Computer/Machine Interface - Biofeedback
Abstract: This paper describes a wearable Brain Computer Interface(BCI) system for drone flight based on Steady State Visual Evoked Potential(SSVEP), which allows users to freely control flight in 3D physical space with immersive first-person view feedback. Our results show that higher accuracy, lower fatigue level is acquired due to the use of head mounted device(HMD) as display compared to monitor display. And the subjects perform good control during preliminary flight tasks.

Keywords: Brain Stimulation - Transcanial magnetic stimulation (TMS), Brain Functional Imaging - Connectivity and Network, Neurological disorders
Abstract: Although repetitive transcranial magnetic stimulation (rTMS) has been applied to various neurological and neuropsychiatric conditions, optimizing the treatment effect still depends on reliably assessing brain state condition. Here, we present our work on optimizing rTMS treatment of a balance disorder, Mal de Debarquement Syndrome (MdDS). With the aim of transferring the fMRI imaging marker to a marker based on a broadly accessible neuro-technology, our study found that network measures of EEG are related to resting state functional connectivity of fMRI. Our findings suggest that integrating EEG with fMRI measures of neural synchrony and functional connectivity may facilitate optimizing rTMS protocols.

Keywords: Brain Functional Imaging - Multimodal, Brain Functional Imaging - Connectivity and Network, Brain physiology and modeling - Neural dynamics and computation
Abstract: Global signal regression (GSR) has been utilized as a pre-processing approach to eliminate the impact of global signal component across the brain in studies using resting-state functional magnetic resonance imaging (fMRI). But the procedure is under debate such that the underlying global signal could be of physiological origin. In this study, we aimed to address the controversy using functional near-infrared spectroscopy (fNIRS), which measures hemodynamic signals by probing local changes in oxygen consumption. Particularly, we acquired simultaneous EEG and fNIRS signals in healthy individuals at eyes open and eyes closed resting state and at different body positions. The present results revealed a negative relation between the changes across eyes-closed and eyes-open in fNIRS global signal amplitude and the changes in EEG vigilance.

Keywords: Brain Functional Imaging - Classification, spatiotemporal dynamics, Brain Functional Imaging - EEG and Evoked Potentials, Neural signal processing
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Keywords: Neural signal processing, Neuromuscular Systems - Peripheral mechanisms, Brain-computer/machine Interface
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Keywords: Human Performance - Attention, Human Performance - Cognition
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Keywords: Neural signal processing, Brain-computer/machine Interface
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Keywords: Neural signal processing, Brain-computer/machine Interface, Neural Interfaces - Computational modeling and simulation
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Keywords: Brain physiology and modeling - Neuron modeling and simulation, Brain physiology and modeling - Neural dynamics and computation, Clinical neurophysiology
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Keywords: Brain-computer/machine Interface, Brain physiology and modeling, Neurorehabilitation - Wearable systems
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Keywords: Brain-computer/machine Interface, Brain-Computer/Machine Interface - Robotics applications
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Keywords: Brain-Computer/Machine Interface - Robotics applications, Human Performance - Gait, Brain Functional Imaging - Classification, spatiotemporal dynamics
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Keywords: Neural Signal Processing - Time frequency analysis, Brain Functional Imaging - EEG and Evoked Potentials, Human Performance - Cognition
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Keywords: Neural Interfaces - Neuroimaging, Neurorehabilitation, Neurological disorders - Stroke
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Keywords: Neural signal processing, Neurorehabilitation, Neuromuscular Systems - EMG models, processing and applications
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Keywords: Neural signal processing, Brain-computer/machine Interface
Abstract: Many real-time brain-machine interface (BMI) applications require quickest detection of abrupt changes in observed neural signals in an online manner. In the presence of multi-neuronal recordings, we propose both model-based and model-free approaches to detect the change in neuronal ensemble spiking activity. The model-based approach is motivated from state space modeling and recursive Bayesian filtering. The model-free approach is motivated from the CUSUM algorithm that computes the cumulative log-likelihood statistics. In the application of detecting the onset of acute thermal pain signals, we validate these approaches using experimental population spike data recorded from freely behaving rats.

Keywords: Brain-computer/machine Interface, Neural signal processing
Abstract: P300-based brain-computer interface (BCI) is one of the most common BCIs. Due to the characteristics of P300 responses vary from person to person, it leads to the necessity of collecting much labeled data from each user and the problem of time-consuming in many applications. In this work, a transfer learning method which dynamically adjusts the weights of instances is applied to improve the P300-based BCI. Offline experiments on BCI competition III and P300 speller with ALS patients dataset prove the robustness of different subjects and the validity when the data of different individuals are sufficient. Online experiments on our P300-based robot control system demonstrated that the classification performance could be enhanced by 13.02% at most compared to the traditional classifiers.

Keywords: Brain physiology and modeling, Brain physiology and modeling - Neural dynamics and computation, Brain Physiology and Modeling - Neural circuits
Abstract: The fundamental role of the cerebellum in motor learning explains the deficits of cerebellar patients in adaptation to a changing environment. For example, lesions to the cerebellar cortex compromise performance during tasks like reaching a target under a force field perturbation. However, the exact relationship between neural damages and misbehaviors still needs to be clarified. To this aim, it could become a turning point to exploit a bio-inspired cerebellar model able to simulate multiple tasks in closed-loop, under physiological and different pathological conditions. In the present study, we used a well-established Spiking Neural Network representing a cerebellar microcomplex to reproduce alterations in a perturbed reaching task, after lesions to the neural population in the cerebellar cortex. Following a multiscale approach, we explored different amounts of damage and analyzed the modified behavior, matching the results of a literature reference study. Then, we could make predictions about the underlying altered neural activity, showing the neural causes of high-level impairments. The results demonstrate the generalization capabilities of the model, extending previous studies on different lesions and tasks. We showed the strong potentialities of computational neuroscience in investigating cerebellar diseases through a non-invasive approach, allowing to isolate damages, test multiple configurations, and suggest treatments thanks to a deeper understanding of pathologies.

Keywords: Neural signal processing, Neural Signal Processing - Time frequency analysis, Brain Functional Imaging - EEG and Evoked Potentials
Abstract: This paper presents a model-based FPGA design for real-time feature extraction of Electroencephalogram (EEG) signals, which can be used for brainwaves bands classification to track and detect mental status in Brain Computer Interface(BCI) applications and consciousness studies. An model-based design approach is used to implement Short-time Fourier Transform (STFT) and extract 20 frequency feature components for classification. These 20 features are divided into 5 groups corresponding to 5 different brainwaves bands. Each feature is defined as the average power spectrum of a number of adjacent frequency components. A hardware model is designed using Xilinx System Generator and implemented on FPGA. Fixed-point is used instead of floating-point to increase operating speed for meeting timing requirement of the real-time system. The design is implemented on a Xilinx Zedboard at 50MHz clock rate, and can be used for up to 128-channel EEG signals feature extraction at 250Hz sample rate.

Keywords: Neural signal processing, Neural Interfaces - Recording, Neurological disorders - Epilepsy
Abstract: The development of automatic detectors for EEG patterns is often challenged by the quality and availability of training events. We have implemented data depuration, augmentation and balancing steps in the development process of a sleep-spindle detector and measured their effect on the detection performance. The training data depuration is based on kernelized k-means clustering and allowed re-grouping training events into a class with similar characteristics. The data augmentation utilizes the multi-channel expression of EEG patterns. The data balancing adjusts the size of the classes so that their size is the same. We worked with 27 EEG recordings which were segmented into epochs of 250ms, each epoch was then characterized by eight features; two EEG recordings were used for training, six for validation and 19 for testing. The depuration of non-augmented, balanced data reclassified 47% of the epochs within visual positive marks and 7% of the epochs outside visual positive marks as belonging to the opposite class. For the detection of single epochs from the validation set, the detector trained with non-augmented, un-balanced, depurated data showed the highest area under the precision-recall curve and the highest Matthews correlation coefficient. For the detection of sleep spindles on the test set, the depuration of non-augmented training data increased Matthews correlation coefficient by 64% and the un-balancing step an additional 1.9%.

Keywords: Neural signal processing, Brain-computer/machine Interface, Brain Functional Imaging - EEG and Evoked Potentials
Abstract: Denoising, noise or interferences are removed from recorded signal to enhance the signal-to-noise ratio (SNR), is a crucial and ubiquitous step in the procedure of signal processing, especially for neurophysiological signal. This step facilitates following processing, such as feature extraction, classification, and data analyses. Conventional methods are based on the principle of separating noise components from the recorded signal and removing them, but these methods do not remove noise completely. In particular, conventional methods seems powerless to eliminate irregular and occasional noise bursts, which are caused by transient electrode contacting problem, head movements, or unpredictable factors. In this paper, we tackled the problem of noise removal from a new perspective, which is opposite to the conventional methods. Data portions that are contaminated by noise are entirely removed and then restored according to their relationships with the remaining signal. The rationale of this procedure is to purify the signal through addition rather than deduction that is normally executed in conventional methods. The results of both synthetic data and real EEG demonstrated that our idea is feasible and provides a new promising manner for noise removal.

Keywords: Neural signal processing, Neurological disorders - Epilepsy, Brain Functional Imaging - EEG and Evoked Potentials
Abstract: Electroencephalography (EEG) of scalp or deep is a signal acquisition tool from electrical discharges of the brain areas. These signals are often accompanied by transient events commonly called interictal paroxysmal events (IPE) or spikes of short durations. In this paper, we propose spike detection method based on Fractal Dimension (FD) using adaptive threshold. After, we extract all spikes detected using a window before applying wavelet filter to make difference between spikes only or spikes with slow wave. Then we will implement a new process using principal components analysis (PCA) before classification to separate the events detected according to their morphologies. Assistance System for identification spike morphology would characterize a link between space-time distribution of IPE and the arrival of the crises.

Keywords: Neural signal processing, Brain-computer/machine Interface, Neural Interfaces - Computational modeling and simulation
Abstract: Electrocorticogram (ECoG) based Brain-Computer Interfaces (BCI) provide finer spatial resolution and improved signal-to-noise ratio than its noninvasive counterpart, Electroencephalogram (EEG). This remarkable feature allows for processing in higher spectral bands in order to elucidate more spatially localized encoding mechanisms. We propose an automatic, fully data-driven method to extract relevant neuromodulation events from single-channel, single-trial traces. In particular, our scheme involves two alternating optimizations that resemble k-means; moreover, correntropy is utilized to provide robust estimation and protection against outliers. In this way, we find distinct behavioral correlates in the low-gamma band (76 - 100 Hz) that encode finger flexion movements in a cued task. The results show that correntropy should be used when working with neuronal oscillations due to the high probability of outliers.

Keywords: Brain physiology and modeling - Neuron modeling and simulation, Brain physiology and modeling - Neural dynamics and computation, Clinical neurophysiology
Abstract: Traumatic brain injuries and damage are major causes of death and disability. Whereas recent experimental evidence has uncovered mechanical phenomena accompanying the neural activity, the mechanism by which mechanical impact affects neuronal impairment remains unclear. We propose a 3D model of a nerve bundle to understand the electrophysiological changes due to trauma. Here, the electrical and mechanical phenomena are simulated simultaneously by using electro-thermal equivalences in the finite element software Abaqus CAE 6.13-3. This model provides a unique framework which combines a real-time fully coupled electro-mechanical, modulated threshold for spiking activation and damage as a function of strain and strain rate. Results show the alteration of electrostriction and neural activity due to damage as observed in experiments. One of the key findings is the distribution of residual stresses and strains at the membrane of each fibre due to mechanically-induced electrophysiological impairments.

Keywords: Brain physiology and modeling - Neuron modeling and simulation, Clinical neurophysiology, Brain physiology and modeling - Neural dynamics and computation
Abstract: Alteration of neuron structure can induce abnormalities in signal propagation for nervous systems, as seen in traumatic brain injuries, damage and tumours. Here, effects of geometrical changes and damage of neuron structure are investigated in two scaled nerve bundle models, made of myelinated and unmyelinated nerve fibres. We propose a 3D finite element model of a nerve bundle as a unique framework in Abaqus CAE 6.13-3, combining a real-time fully coupled electro-mechanical, modulated threshold for spiking activation and independent alteration of the electrical properties for each 3-layer fibre. The insulation sheath of myelin constricts the membrane deformation and lower strain levels are found at the nerve membrane in the myelinated bundle. The significance of this study is the difference in distribution of residual stresses and strains at the membrane for the different sizes and types of fibres.

Keywords: Brain-computer/machine Interface, Brain physiology and modeling, Neurorehabilitation - Wearable systems
Abstract: Across neuroscience research, clinical diagnostics, and engineering applications in pain evaluation and treatment, there is a need for an objective measure of pain experience and detection when it occurs. This detector should be reliable in real-world settings using non-invasive data sources. We present a simple yet robust paradigm for decoding pain with neural and physiological data including EEG, pulse, and skin conductance measurements, using multivariate classification to distinguish painful events from non-painful multimodal sensory stimuli. We employed a sparse logistic regression machine learning protocol with automatic feature selection. EEG input consisted of time-frequency changes under trial conditions, and physiological data included fluctuations in pulse and skin conductance. Classification averaged 70% accuracy and selected between 5 and 15 features. In our experiment, pain was induced by cold stimulation which became noxious with prolonged exposure. Due to the nature of the stimulus along with individual variability in pain sensitivity, we did not observe specific rapid evoked responses across participants. However, this format more closely resembles the experience of pain conditions requiring intervention which could be facilitated by a decoding system. The results illustrate the feasibility of developing a wireless pain detection system and give insight to important temporal, spectral, and spatial EEG events and physiological indicators of pain states.

Keywords: Brain physiology and modeling - Neural dynamics and computation, Neurological disorders - Diagnostic and evaluation techniques, Brain Stimulation-Deep brain stimulation
Abstract: In the sensory thalamus and periventricular gray/ periaqueductal gray (PVAG) nucleus, the synchronization level of multiple frequency band oscillations of local field potentials (LFPs) have been shown to be associated with chronic pain perception and modulation.In this study,a state identification approach was generated to dynamically identify the synchronization state of neural oscillation.In this approach,a pattern extraction model was created to characterize the patterning of the neural oscillations based on wavelet packet transform.The value of wavelet packet coefficients represents the synchronization level of pattern. And then a state discrimination model was designed to distinguish the synchronization state and de-synchronization state of pattern based on calculating a suitable threshold and discrimination strategies.By using the sensory thalamus and PVAG LFPs of neuropathic pain and simulation signals,the parameters of the approach were optimized for theta pattern and alpha pattern identification respectively.Then this approach was applied to the sensory thalamus and PVAG LFPs,and was able to identify the synchronization state of theta and alpha pattern.This study provides a reliable approach to dynamicallly identify the synchronization level of pattern of neuropathic pain disease through optimizing the parameters. Based on this approach, a real-time monitoring of the pain state and an adaptive treatment regimen can be achieved.

Keywords: Brain-computer/machine Interface, Brain-Computer/Machine Interface - Robotics applications
Abstract: Visual-evoked potential (VEP)-based BCIs have been shown to provide the highest information transfer rates and reliability among BCI approaches. However, to date, no flexible software platform exists that allows investigators and end-users to easily evaluate and optimize VEP stimulus parameters such as size, position, flashing rate, color, etc., with seamless integration to an application environment. This paper provides an overview of the development of such a customizable VEP-BCI software platform called Visual Evoked Stimulation and SELection Software (VESSELS), and its implementation for control of a motorized wheelchair.

Keywords: Neural Signal Processing - Time frequency analysis, Brain physiology and modeling, Neurological disorders - Epilepsy
Abstract: Surgical removal of seizure-generating brain tissue can cure epilepsy in patients who do not respond to medications. However, identifying seizure-generating regions is difficult and fails in many cases. In this paper, we report a fully unsupervised and automated approach to seizure focus localization using a Bayesian filter. This method uses a spectral domain feature, Power in Bands (PIB). PIB is extracted from inter-ictal (non-seizure) intracranial EEG recordings of patients with focal epilepsy to differentiate normal and abnormal brain regions. This study was carried out using data collected from 34 patients with focal epilepsy at the Mayo Clinic. Experiments show that using a Bayesian filter for capturing temporal properties of the iEEGs recorded from epileptic brains remarkably improves localization accuracy (AUC: 0.63 -> 0.72). Our study also reaffirms that high-frequency oscillations and inter-ictal spikes are useful inter-ictal biomarkers of the epileptic brain, and PIB, which could be implemented with relatively low computational burden, performs as well as the standard bio-markers when used in this setting. We conclude that the technique of extracting spectral features from inter-ictal iEEGs and capturing their temporal properties via a Bayesian filter markedly improves our ability to localize seizure onset zones.

Keywords: Brain-computer/machine Interface, Neural signal processing, Motor neuroprostheses
Abstract: A common problem in Brain-Machine Interface (BMI) is the variations in neural signals over time, leading to significant decrease in decoding performance if the decoder is not re-trained. However, frequent re-training is not practical in real use case. In our work, we found that a temporally more robust system may be achieved through the use of wavelet transform in feature extraction. We used wavelet transform coefficients as means to detect spikes in neural recordings, in contrast to conventional amplitude threshold methods. Using offline data as the preliminary testbed, we showed that decoding based on firing rates determined from four levels of wavelet transform decomposition resulted in a decoder with 6-12% improvement in accuracy sustained over four weeks after training. This strategy suggests that wavelet transform coefficients for spike detection may be more temporally robust as features for decoding, and offers a good starting point for further improvements to tackle nonstationarities in BMI.

Keywords: Brain-computer/machine Interface, Neural signal processing, Clinical neurophysiology
Abstract: This work proposes a Common Spatial Pattern with Polarity Check (CSPPC) to facilitate Movement Related Cortical Potential (MRCP) detection. The algorithm was compared with the Locality Preserving Projection (LPP) algorithm in the context of detecting foot dorsiflexion within a group of thirteen subjects. It has been shown that CSPPC achieved a significantly reduced delay latency compared to LPP (-25.9±190.7 ms vs. 204.6±123.7 ms), which had a similar detection accuracy (true positive rate: 73.6±23.3% vs. 72.2±16.3%). This proposed algorithm will enhance the induction of neuroplasticity by significantly reducing the delay between movement detection and the corresponding afferent input.

Keywords: Brain-Computer/Machine Interface - Robotics applications, Human Performance - Gait, Brain Functional Imaging - Classification, spatiotemporal dynamics
Abstract: Restoring normal walking abilities following the loss of them is a challenge. Importantly, there is a growing need for a better understanding of brain plasticity and the neural involvements for the initiation and control of these abilities so as to develop better rehabilitation programmes and external support devices. In this paper, we attempt to identify gait-related neural activities by decoding neural signals obtained from electroencephalography (EEG) measurements while subjects performed three types of walking: without exoskeleton (free walking), and with exoskeleton support (zero force and assisting force). An average classification accuracy of 92.0% for training and 73.8% for testing sets was achieved using features extracted from mu and beta frequency bands. Furthermore, we found that mu band features contributed significantly to the classification accuracy and were localized mainly in sensorimotor regions that are associated with the control of the exoskeleton. These findings contribute meaningful insight on the neural dynamics associated with lower limb movements and provide useful information for future developments of orthotic devices and rehabilitation programs.

Keywords: Brain-computer/machine Interface, Neural signal processing, Human Performance - Attention
Abstract: Dual tasking refers to the simultaneous execution of two tasks with different demands. In this study, we aimed to investigate the effect of a second task on a main task of motor execution and on the ability to detect the cortical potential related to the main task from non-invasive electroencephalographic (EEG). Participants were asked to perform a series of cue-based ankle dorsiflexions as the primary task (single task level). In some experimental runs, in addition to the primary task they concurrently attended an auditory oddball paradigm consisting of three tones while they were asked to count the number of sequences of special tones (dual task level). EEG signals were recorded from nine channels centered on Cz. Analysis of event-related potential (ERP) signals from Cz confirmed that the oddball task decreased the attention to the ankle dorsiflexion significantly. Furthermore, movement-related cortical potential (MRCP) analysis revealed that the amplitude of the MRCP and pre-movement slopes were changed significantly. These variations were significantly greater for the EEG channels corresponding to the motor cortex and the frontal-central cortex.

Keywords: Neural Signal Processing - Blind source separation, Brain functional imaging
Abstract: The default mode network (DMN) is suggested to be associated with conscious and the connectivity of DMN persists during light sleep. The aim of this study is to find change of the information flow through the DMN across the sleep/wake cycles with the methods of the isolated effective coherence (iCoh) and the tensor decomposition. We observed that during the sleep process there existed two opposite information flow change patterns in the DMN, one is a posterior-to-anterior information flow in the higher-frequency band (>25Hz) and the other is an anterior-to-posterior information flow in the lower-frequency band (<25Hz). This suggests that the DMN may play an important role during the sleep/wake cycles.

Keywords: Brain-computer/machine Interface, Neural signal processing, Human performance
Abstract: Abstract—Target image detection based on rapid serial visual presentation (RSVP) paradigm is a typical Brain–computer interface (BCI) with various applications, such as image retrieval. In an RSVP paradigm, the P300 component is detected to determine the target image, which requires high-precision single-trial P300 detection methods. However, compared to multi-trial P300 detection methods, the performance of single-trial methods are always relatively lower. In this paper, we propose a novel paradigm, triple-RSVP for EEG-based target image detection. In the triple-RSVP, three images appear at the same time, and target image will appear three times, so that multi-trial P300 classification methods can be used to improve the detection accuracy. Experimental results show that the accuracy of the triple-RSVP is superior to standard RSVP (single RSVP) and dual-RSVP (Wilcoxon signed rank test, p<0.05).

Keywords: Neural Signal Processing - Time frequency analysis, Brain Functional Imaging - EEG and Evoked Potentials, Human Performance - Cognition
Abstract: This paper describes the importance of theta frequency oscillations through analysis in emotion regulation and in various oscillatory patterns with reappraising sad and fear stimuli. Previous research suggests that the processing of fear and sadness has different neural bases, and that dynamics in theta frequency is particularly sensitive to the processing of emotional stimuli. However, it remains unknown whether theta oscillations vary in emotion regulation processes as a function of discrete emotions. We, therefore, explored theta oscillatory patterns of cognitive reappraisal for sad and fearful stimuli by spectrum analysis. EEG from 24 healthy volunteers was recorded correspondingly while they were asked to passively watch or reappraise the content of pictures with sad or fear emotion. In the first “watching” task, volunteers were asked to only watch the stimulus attentively. In the later “regulation” tasks, the volunteers, however, were asked to engage in cognitive reappraisal and decrease the negative interpretation of the presenting scenes. Through our analysis of the results, we realized that fearful stimuli were invoked stronger than sad stimuli in both “watching” and “regulation” tasks. Furthermore, the reappraisal or fearful scenes could decrease the theta oscillations more in “regulation” task than in “watching” task with fearful stimuli.

Keywords: Brain-computer/machine Interface, Neural signal processing, Human performance
Abstract: Abstract—As a new biometric, the Electroencephalogram(EEG) signal has the advantages of invisibility, non-clonability, and non-coercion compare to traditional biometrics. However, the real-time and stability are the difficulties that the current EEG-based person authentication systems face. In this paper, we design a real-time and stable person authentication system using EEG signals, which are elicited by self- and non-self-face rapid serial visual presentation (RSVP). Convolutional neural network (CNN) is applied to dig the specific feature of different individuals. The mean accuracy of 85.03% and 91.27% is achieved with the login time of 3 seconds and 6 seconds respectively, which illustrates the precision and real-time of the system.

Keywords: Neural Interfaces - Neuroimaging, Neurorehabilitation, Neurological disorders - Stroke
Abstract: In this paper, we compared the performance of a number of Diffusion Tensor Imaging (DTI) pre-processing tools on a dataset of chronic subcortical stroke patients during rehabilitation exercise. In the comparison, acquired Diffusion- Weighted Images (DWI) are firstly pre-processed by each pipeline (with different tools) independently. Then a DTI measure, FA (fractional anisotropy), is derived from each processed image, and a group-based DTI tractography analysis tool, Tract-Based Spatial Statistics (TBSS), is used to localize brain changes in white matter that correlate to the behavior changes during rehabilitation. Although the ground-truth of the dataset is unavailable, it can be observed that there exist significant variations in the obtained hot spot maps that come with different DTI pre-processing pipelines. It suggests that the imaging technicians and scientists should choose the tools carefully according to the acquisition methods and parameters.

Keywords: Brain-computer/machine Interface, Neural signal processing, Brain-Computer/Machine Interface - Robotics applications
Abstract: Electroencephalography (EEG) processing methods mostly focus on extracting its spectral or spatial features, which are proven to discriminate bilateral hand movement, hand movement directions and speed. The focus of current study is to explore EEG time-domain features that represent neural correlates of hand movement execution speed. In this paper, we propose autocorrelation analysis of EEG and features derived from it that utilizes difference in execution time of fast v/s slow tasks. The variation in decay constant of autocorrelation of EEG over execution time is studied, and its application as a potential feature to discriminate movement speed is explored. The proposed analysis method has been validated on EEG data recorded from 7 subjects performing right hand movement at two different speeds. An average classification accuracy of 75.71% and 85.16% is obtained, using features derived from significant time segments in the data.

Keywords: Neural signal processing, Neurological disorders - Epilepsy
Abstract: Functional brain connectivity (FBC) network has been used for characterizing the dynamics of seizure evolution. In this paper, FBC networks based on correlation coefficient (COR), cross-correlation (xCOR), coherence (COH), phase slope index (PSI), phase locking value (PLV), phase lag index (PLI), mutual information (MI), transfer entropy (TE), Granger-causality index (GCI), directed transfer function (DTF), and partial directed coherence (PDC), were constructed. Graph theoretic measures, including transitivity, modularity, characteristic path length, radius, diameter, and global efficiency were calculated for each FBC network, and used as the feature vector for classifying ECoG signals corresponding to seizure and nonseizure. The results show that an average accuracy of 95.3% was obtained using the combined features from all FBC networks.

Keywords: Neural Signal Processing - Time frequency analysis
Abstract: Thanks to recent improvements in the neuro-technology, parallel recordings with an ever-increasing number of micro-transducers are now available to monitor the neuronal spiking activity of large-scale neuronal networks. At the same time, continuous improvements are required to develop computationally efficient software for processing and analyzing such huge amounts of data. In this work, we present a new tool named SPICODYN, as a possible solution to efficiently process and analyze big-data coming from in vitro multi-site recordings. By adopting the standardized HDF5 raw input data format it offers independency from the specific acquisition setup. SPICODYN allows performing pre-processing operations (spike detection), full dynamics and functional-effective connectivity analysis on the generated spike trains, and topological characterization related to the estimated connectivity.

Keywords: Neural Signal Processing - Time frequency analysis, Brain Stimulation-Deep brain stimulation, Neural Interfaces - Neural stimulation
Abstract: Parkinson’s disease (PD) is a progressive, neurodegenerative disorder, characterized by hallmark motor symptoms. Deep brain stimulation (DBS) has been used to treat advanced PD successfully. Previous studies have found that the DBS also has an effect on the electrophysiological activity of the deep brain nucleus while alleviate the PD symptoms. Here, in an attempt to gain a greater understanding of dynamic response of neural activity during subthalamic nucleus (STN) DBS for PD, local filed potentials (LFPs) were recorded from the STN during closed-loop DBS. The time frequency analysis methods short-time Fourier transform and continuous wavelet transform were used to detect the dynamic change of LFPs and the related factors which affect the length of stimulation time. The results suggest that both alpha activity and beta activity are dynamic change with electric stimulation. The delay time of DBS inhibit beta activity is about 160 ms. These results also demonstrated that the length of stimulation time are associated with the baseline amplitude, the average amplitude and the peak amplitude of beta activity. Studying the response of neural activity to electrical stimulation can reveal the electrophysiological mechanisms of DBS. Furthermore, it can improve the treatment of closed-loop DBS for PD and promote the development of intelligent neural modulation.

Keywords: Brain-computer/machine Interface, Neural signal processing
Abstract: In Brain-Computer Interfaces (BCIs), with multiple recordings from different subjects in hand, a question arises regarding whether the knowledge of previously recorded subjects can be transferred to a new subject. In this study, we explore the possibility of transferring knowledge by using a convolutional network model trained on multiple subjects and fine-tuning the model on the new subject with labels estimated by the trained model. Our results show a significant increase in 4-class classification accuracy on the BCI IV-2a competition data when a small subset of the data is provided for training.

Keywords: Neural Signal Processing - Blind source separation, Neural signal processing, Neural Signal Processing - Nonlinear analysis
Abstract: Traditional Electrooculogram (EOG) artifact removal methods are usually based on the correlation information of multi-channel electroencephalogram (EEG), which are difficult to apply to single-channel portable brain computer interface (BCI). This paper presents an EOG separation method which combines variational mode decomposition (VMD) and independent component analysis (ICA) called VMD-ICA. Firstly, VMD technique is applied to the raw EEG dataset to obtain the IMFs components. Then, FastICA is used to gain the independent components of all the IMFs. Finally, the independent component which has the maximum significant coherent area (SCA) between independent components and the reference regarded as EOG components are eliminated automatically. We confirmed that our proposed method could eliminate EOG artifact in single-channel EEG signals effectively.

Keywords: Brain physiology and modeling - Neuron modeling and simulation, Brain physiology and modeling, Brain physiology and modeling - Neural dynamics and computation
Abstract: The ability to detect noxious stimuli and evoke a defensive response is made possible by specialized peripheral sensory neurons called nociceptors that respond to stimuli such as extreme temperature, pressure and injury. A number of experimental studies have been done on nociceptors at various range of temperatures in order to understand the underlying biophysical mechanisms. Simple models for describing temperature sensitivity are available, such as the Hodgkin and Huxley model for squid giant axon. However, to our knowledge there are no existing models for temperature sensitive nociceptive C-fiber DRG neurons with multiple ion channels that have also been validated against experimental findings for different temperatures. Here, we report a biophysical model for temperature dependence in nociceptors, more precisely for Cfibers. We validate the model using action potentials recorded at different temperatures. The resulting action potentials from the simulation show that the model satisfactorily replicates the key features of the experimentally obtained results paving the way for further studies

Keywords: Brain-computer/machine Interface, Neural signal processing, Brain Functional Imaging - EEG and Evoked Potentials
Abstract: Sparse Bayesian Learning (SBL) is a widely used framework which helps us to deal with two basic problems of machine learning, to avoid overfitting of the model and to incorporate prior knowledge into it. In this work, multiple linear regression models under the SBL framework are used for the problem of multiclass classification when multiple subjects are available. As a case study, we apply our method to the detection of Steady State Visual Evoked Potentials (SSVEP), a problem that arises frequently into the Brain Computer Interface (BCI) paradigm. The multiclass classification problem is decomposed into multiple regression problems. By solving these regression problems, a discriminant vector is learned for further processing. In addition the adoption of the kernel trick and the special treatment of produced similarity matrix provides us with the ability to use a Leave-One-Subject-Out training procedure resulting in a classification system suitable for subject independent classification. Extensive comparisons are carried out between the proposed algorithm, the SVM classifier and the CCA based methodology. The experimental results demonstrate that the proposed algorithm outperforms the competing approaches, in terms of classification accuracy and Information Transfer Rate (ITR), when the number of utilized EEG channels is small.

Keywords: Brain-computer/machine Interface, Neural signal processing
Abstract: Brain waves contain fundamental information about cortical activity: signal power within certain frequency bands, which is exploited by a variety of Brain-Computer Interface applications. For real-time systems, these features must be estimated as quickly as possible while maintaining high signal fidelity. Here, we present a statistically optimal signal processing framework for real-time bandpower estimation and tracking. Key components are a spectral shaping stage for increased sensitivity and Kalman filtering of log-transformed bandpower estimates for optimal tracking. The system has one degree of freedom, which allows for adaptive design based on signal dynamics. The overall complexity remains low.

We evaluated the proposed architecture based on two experiments involving cortical motor functions and receptive-language related cortical areas. First results are promising. Spectral shaping based on a whitening transform increases the sensitivity (z-Score) up to 60%. Furthermore, the tracking time lag is substantially reduced relative to conventional approaches.

Keywords: Brain-computer/machine Interface, Neural signal processing, Human Performance - Fatigue
Abstract: Despite the recent increasing interest in biometric identification using electroencephalogram (EEG) signals, the state of the art still lacks a simple and robust model that is useful in real applications. This work proposes a new approach based on convolutional neural network CNN. The proposed CNN works directly on raw EEG data, thus alleviating the need for engineering features. We investigate the performance of the CNN on datasets of 100 subjects collected from one driving fatigue experiment. Our results show that the CNN model is fast highly efficient in training (<0.5h on >100K training epochs) and highly robust, achieving 97% accuracy in identifying ~14K testing epochs from 100 subjects with non-time-locked natural driving fatigue data and much higher than from randomly sampled epochs (90%). Overall, this work demonstrates the potential of deep learning solutions for real-life EEG-based biometric identification.

Keywords: Neural signal processing, Brain physiology and modeling - Neural dynamics and computation
Abstract: Generalized linear models (GLMs) are useful tools to capture the characteristic features of spiking neurons; however, the long-term prediction of an autoregressive GLM inferred through maximum likelihood (ML) can be subject to runway self-excitation. We explain here that this runaway excitation is a consequence of the one-step-ahead ML inference used in estimating the parameters of the GLM. Alternatively, inference techniques that incorporate the likelihood of spiking multiple steps ahead in the future can alleviate this instability. We formulate a multi-step log-likelihood (MSLL) as an alternative objective for fitting spiking data. We maximize MSLL to infer an autoregressive GLM for individual spiking neurons recorded from the lateral intraparietal (LIP) area of monkeys during a perceptual decision-making task. While ML inference is shown to produce a GLM with poor fits of the neuron's interspike intervals and autocorrelation, in addition to its runaway excitation, MSLL fit models show a substantial improvement in interval statistics and stable spiking.

Keywords: Neural signal processing, Neuromuscular Systems - Peripheral mechanisms, Brain-computer/machine Interface
Abstract: Real time algorithms for decoding physiological signals from peripheral nerve recordings form an important component of closed loop bioelectronic medicine (electroceutical) systems. As a feasibility demonstration, we considered the problem of decoding bladder pressure from pelvic nerve electroneurograms. We extracted power spectral density of the nerve signal across a band optimised for Shannon Mutual Information, followed by linearization via piece-wise linear regression, and finally decoded signal reconstruction through optimal linear filtering. We demonstrate robust and effective reconstruction of bladder pressure, both prior to and following pharmacological manipulation.

Keywords: Brain Functional Imaging - Classification, spatiotemporal dynamics, Brain Functional Imaging - EEG and Evoked Potentials, Neural signal processing
Abstract: Electroencephalography has been studied to understand various brain functions. These functions can be related to their frequency and spatial patterns. These properties are relatively unknown infants. The first year of life includes stages of significant growth neurologically and behaviorally. In the present study, it is aimed to investigate infant brain development using high-density EEG(124 channel) collected on a weekly basis from 5 to 7 months of age. Spectral power and spatial topography of infant EEG were used to study the changing nature of infant brains during that time period. Data driven clustering analysis was used to blindly classify EEG spatial topographies as functions of frequency and time into different classes. Our results indicate that more differences in EEG spatial topographies are revealed in frequency domain than time domain (5-7 months). However, dynamic changes are further revealed in the spectral properties of classified spatial topographies, including shifts of frequency boundary and peak frequency, along the EEG spectrum (i.e., delta, theta, and alpha bands). These data provide insights on spectral structure of infant EEG and their dynamic patterns along maturation.

Keywords: Human Performance - Attention, Human Performance - Cognition
Abstract: It is known that signs of early auditory selective attention are reflected in the N1-wave of auditory late potentials. In recent years, we used instantaneous phase synchronization measures related to this N1-effect to assess the attentional effort in listening. In particular, we showed that listening effort induced by task difficulty can be quantified by using this method. Subsequently, in order to not be restricted to short transient stimuli in event--related paradigms, we translated the idea of an objective listening effort estimation to the ongoing EEG activity in arbitrary, non-event-related listening tasks. Here we could again quantify effortful listening by analyzing the circular organization of the ongoing instantaneous phase. In this paper, we apply for the first time circular measures to segmented ERP and unsegmented EEG data from a repetition of the seminal dichotic tone detection experiment of Hillyard et al. who first described the N1-effect. In particular, we show that correlates of selective attention can be extracted from both data types by assessing the organization of the instantaneous phase. It is concluded that our study suggests a unified framework to analyze neural correlates of selective attention in ERPs and the ongoing EEG activity.

Keywords: Brain-Computer/Machine Interface - Robotics applications, Brain physiology and modeling - Neural dynamics and computation, Neural signal processing
Abstract: This paper reports how to train a Deep Belief Network (DBN) using only 8-bit fixed-point parameters. We propose a dynamic-point stochastic rounding algorithm that provides enhanced results compared to the existing stochastic rounding. We show that by using a variable scaling factor, the fixed-point parameter updates are enhanced. To be more hardware amenable, the use of common scaling factor at each layer of DBN is further proposed. Using publicly available MNIST database, we show that the proposed algorithm can train a 3-layer DBN with an average accuracy of 98.49%, with a drop of 0.08% from the double floating-point average accuracy.

Keywords: Neural signal processing, Neurorehabilitation, Neuromuscular Systems - EMG models, processing and applications
Abstract: To effectively restore the fine motor functions of the forearm and hand of stroke survivors and patients with traumatic brain injury (TBI), recent studies have proposed an active rehabilitation concept based on the pattern recognition of electromyography (EMG) signals to decode the motor intent of the patients. The results from these studies suggested that pattern recognition of EMG signals associated with the limb motions could potentially aid the development of active rehabilitation robots. To obtain richer set of neural information from multiple-channel EMG recordings, this study proposed a muscle synergies based method for motor intent identification from high-density EMG signals recorded from eight TBI subjects. For baseline comparison, the linear discriminant analysis (LDA) based pattern recognition approach was also examined. The outcomes show that the proposed muscle synergy based method outperformed the commonly used LDA with more centralized distribution of motion classification accuracy across all the TBI subjects. And such an increment in accuracy suggests the feasibility of using muscle synergies for neural control in active rehabilitation for TBI patients.

Keywords: Neural Signal Processing - Nonlinear analysis, Brain-computer/machine Interface, Human Performance - Cognition
Abstract: Verbal communication makes human unique from other species. People use different modalities of speech while communicating with others. Widely practised modalities are speak loudly (utter), whispering and mumbling with closed lips. Apart from speaking, people also speak in their mind. Due to different ailments or injury, some people have lost their ability to speak and are forced to take other means to communicate. Speech restoration through Brain Computer Interfacing (BCI) is still at nascent stage. Through this study, we have explored the contrast between these modalities and it will lead to identification of imagined speech through electroencephalography (EEG). As different speech modalities are similar in nature in spatiotemporal domain, here we have proposed utilisation of nonlinearilty, more specifically multifractal nature, of the modalities present in EEGs. On the basis of the multifractal parameters we have achieved 99.7% accuracy in classification.

Keywords: Neural signal processing, Human Performance - Cognition, Neural Signal Processing - Time frequency analysis
Abstract: Working memory processing is central for higherorder cognitive functions. Although the ability to access and extract working memory load has been proven feasible, the temporal resolution is low and cross-task generalization is poor. In this study, EEG oscillatory activity was recorded from sixteen healthy subjects while they performed two versions of the visual n-back task. Observed effects in the working memory-related EEG oscillatory activity, specifically in theta, alpha and low beta power, are significantly different in the two tasks (i.e. two categories of visual stimuli) and these differences are greatest after image onset. Furthermore, cross-task generalization can be obtained by concatenating both tasks and although similar performances are observed before and after image onset, this study highlights the complexity of working memory processing related to different categories of visual stimuli, particularly after image onset, that are crucial to understand, in order to interpret the extraction of working memory load.

Keywords: Neural signal processing
Abstract: This work proposed to estimate robust network patterns with Bayesian analysis. Especially, the priori information was imposed on the covariance matrix which characterizes the linkage strength of the network. The simulation results demonstrated that our proposed method could suppress the noise influence efficiently.

Keywords: Brain physiology and modeling - Neuron modeling and simulation, Brain physiology and modeling, Brain physiology and modeling - Neural dynamics and computation
Abstract: Satellite glial cells (SGCs) are supporting cells enveloping and isolating soma of neurons in sensory ganglia such as dorsal root ganglion (DRG) in the peripheral nervous system. Recent studies have shown that they are involved in intercellular communication between neuronal somata within ganglia in chronic pain and inflammatory conditions. One hypothesis proposed for this communication is via release of adenosine triphosphate (ATP) into extracellular region between soma and its SGCs. ATP release activates adjacent SGCs which then transfer their activity to other non-activated SGCs via gap junctions. The activated SGCs then release ATP into the extracellular space surrounding the inactive soma leading to its activation. We tested this hypothesis by using a model with 2 DRG neuron somata, their adjacent SGCs connected via gap junctions. All cells were endowed with P2X3 receptor (for ATP) along with release and uptake mechanisms of ATP. The model showed that release of ATP from one DRG neuron soma can induce activity in the neighbouring neuron soma via SGCs. Hence, neuromodulation of the components of such communication can be explored for pain relief.

Keywords: Brain physiology and modeling - Neural dynamics and computation, Neural Signal Processing - Nonlinear analysis, Human Performance - Cognition
Abstract: We developed a technique showing that non stationarities in EEG signal carry information about cognition. This technique was successfully tested in two different databases: a working memory database, and an Alzheimer disease database. We also provide evidence suggesting that EEG might not be even piecewise stationary. Therefore, as changes between different stationary regimes are linked to transitions between metastable states in the brain, transitions between those states might not occur in a discrete manner, after a short period of metastability, but rather in a continuous way. Transitions between neighbouring states would occur more often, whereas large transitions occur as well. Large transitions suggesting discreteness had been detected by other techniques, but small fluctuations are not noise, as they can be successfully used to infer aspects of cognition.

Keywords: Neural signal processing, Neural Signal Processing - Nonlinear analysis, Brain physiology and modeling - Neural dynamics and computation
Abstract: How the brain maintains the stability of visual perception across saccade is a central question in systems neuroscience; accurately characterizing visual responses in the perisaccadic period is an important step towards understanding how the visual world is represented during saccades. Here, we develop a probabilistic model in the Generalized Linear Model framework to characterize and predict the pre-, trans-, and post-saccadic responses of single Middle Temporal (MT) neurons at the level of single-trial spike trains. We fit the MT spike response to a model, consisting of a set of stimulus kernels capturing neuron’s spatiotemporal filtering properties, a set of saccade kernels capturing changes in stimulus sensitivity induced by eye movement, an offset kernel capturing time-varying baseline activity relative to saccade, a post-spike kernel capturing dependencies on spiking history, a set of coupling kernels capturing correlations between neurons, and finally an LFP kernel capturing effects of brain state in generating spike responses. The model, with parameters fit directly to data, accurately predicts responses to novel stimuli on individual trials during saccadic eye movements. The model can also be used to optimally decode time-varying visual information carried by MT responses, thus providing a tool for reading out visual information and understanding the representation of visual scene during eye movements.

Keywords: Brain physiology and modeling, Neural Signal Processing - Nonlinear analysis, Brain Functional Imaging - EEG and Evoked Potentials
Abstract: In support of a method for nonlinear modeling of cortical connectivity, an innovation in evolutionary processing is reported. By a strategic modification to the crossover and mutation operators within the NSGA-II genetic algorithm, the number of generations required to achieve optimal nonlinear regressor models with 99.9% confidence is reduced by 52%.

Keywords: Neural Signal Processing - Blind source separation, Neural Signal Processing - Time frequency analysis, Neural signal processing
Abstract: In order to understand brain mechanisms and functionalities, neural probes with electrode arrays are incorporated into mice and Local Field Potentials (LFP) are recorded indicating the activities of groups of neurons. Next, the brain activity can be analyzed in terms of Current Source Density (CSD), which are computed via the LFP. In this paper, we propose the analysis of the somatosensory cortex signals of a mouse applying Blind Source Separation (BSS) schemes. In contrast to the standard CSD, we show that signal separation using BSS schemes can be useful to identify groups of neurons of different layers of the somatosensory cortex that are associated. Another contribution of this work is to propose the use of the PARAFAC model on the analysis of somatosensory cortex signals, whose results are consistent with results obtained via Spatiotemporal Independent Component Analysis (stICA).

Keywords: Brain-computer/machine Interface, Neural signal processing, Neural Signal Processing - Time frequency analysis
Abstract: Motor imagery based BCIs are one of the most important BCI paradigms. Although it has been studied for a long time, the EEG features for kinetic information of motor imagery are still less known. In this paper, we explored EEG patterns of hand force motor imagery. Six subjects participated in this study, who were required to imagine clenching their hands with two different levels of force during the experiment. Time-frequency analyses showed a significant decrease of EEG power at alpha and beta band when subjects performed the motor imagery task, compared to the rest state. Furthermore, the power decrease of the high force imagery was significantly larger than that of the low force imagery. Support vector machines were used to classify the three different EEG patterns (rest vs. high force vs. low force) and achieved an accuracy of 71% on average. It suggests that the force level of motor imagery plays a critical role in the patterns of event-related desynchronization, and could be used to control BCIs.

Keywords: Neural signal processing, Neural Interfaces - Computational modeling and simulation, Brain-computer/machine Interface
Abstract: Decoding intended movement trajectory from neural activity is crucial for developing neuroprosthetic devices. In this study, we propose a processing framework to combine different information from two types of neural activities: action potentials (spikes) and local field potentials (LFPs). For this purpose, we proposed a stacked generalization approach based on recurrent neural network to enhance decoding accuracy of movement kinematics. We examined decoding performance of the proposed stacked recurrent neural network (SRNN) on decoding of reaching movement using intracortical datasets. The results show that the stacked generalization approach can enhance the decoding performance and can be used as an information fusion tool for multi-modal neuroprosthetic devices.

Keywords: Neural signal processing, Clinical neurophysiology
Abstract: Electroencephalography (EEG) has the potential to be used in clinical practice for prediction of subjective pain levels. But the performance of a prediction model trained from one dataset is normally degraded when being used on other datasets. In this study, we proposed to apply transfer components analysis (TCA) to increase the model transfer ability and to improve the accuracy of multi-center EEG-based pain prediction.

Keywords: Neural Signal Processing - Time frequency analysis, Brain physiology and modeling - Neural dynamics and computation, Neurological disorders
Abstract: Neural oscillations at the level of the rodent motor cortex are widely studied. However, not much is known about electrical activity originating in a lesioned tissue.

Here, we present results from a group of 16 animals that underwent surgery to induce a cortical lesion in the area of the motor cortex corresponding to their most dexterious limb. The animals were then observed under a free moving task. Brain activity at the site of the lesion was compared between moments of locomotion and moments of absolute rest.

We report significant increases of theta and gamma oscillatory activity correlated with an engaged movement state. Further work will focus on relating the nature of this theta-gamma activity with more detailed behavior features, such as kinematic features of the behaviour, or the motor impairment the animal exhibits.

Keywords: Neural signal processing, Neural Interfaces - Neuromorphic engineering, Neural Interfaces - Neural microsystems and Interface engineering
Abstract: In this paper we introduce a new spike-sorting method using an STPD neural network. By using the STDP rules appropriately, the network is able to perform online unsupervised learning and to automatically sort action potentials with a good performance.

Keywords: Neural Signal Processing - Nonlinear analysis, Neurological disorders - Epilepsy, Brain physiology and modeling
Abstract: The functional connectivity network (FCN) of brain can be measured by using phase synchronization (PS) of a pair of EEG channels. We compare two methods for measuring PS. Results show that the proposed measure based on the correlation between probabilities of recurrence (CPR), outperforms a conventional frequency-domain measure and show statistical difference in all morphological seizures.

Keywords: Brain-Computer/Machine Interface - Robotics applications, Neural Interfaces - Implantable systems, Motor Neuroprostheses - Robotics
Abstract: One of the major challenges in the field of neuroprosthetics is the development of a clinical Brain Computer Interface (BCI) system with high performances. To address this challenge, CEA/LETI/CLINATEC is currently conducting a project to develop an ElectroCorticoGram (ECoG)-based BCI platform for chronic use in clinical applications. The goal of our BCI Project is to bring the proof of concept that it will be feasible for a tetraplegic subject to control complex effectors (such as a 4-limb exoskeleton) after training, thanks to his cortical brain electrical activity decoding. A clinical BCI platform based of the chronic ECoG recording implant WIMAGINE®, a set of BCI decoding algorithms and dedicated softwares, and a 4-limb exoskeleton EMY, was developed and autorized for a clinical used.

Keywords: Brain-Computer/Machine Interface - Robotics applications, Motor Neuroprostheses - Robotics
Abstract: Intracortical Brain Computer Interfaces (iBCI) are a promising avenue by which people with tetraplegia may intuitively gain independent control of computer cursors and robotics/prosthetics. A valuable feature for an iBCI system is the ability to switch between multiple effectors without intermediate intervention. However, it has been noted that there is a drastic decline in control performance when switching effectors. We sought to understand whether an underlying context dependent network functionality within motor cortex could explain these findings.

A 52-year-old man with tetraplegia had two 96 channel microelectrode arrays placed in left precentral gyrus as part of the BrainGate2 pilot clinical trial. He performed a two-dimensional task in which he moved a computer cursor or robotic arm over one of eight targets. We retrospectively analyzed the variation in directional tuning of channels as control of the effectors was alternated. Spike Train Similarity Space based classification analysis was used to evaluate differences in ensemble activity patterns.

We identified three distinct groups of neurons. One set of neurons presented a near-perfect separation between activity patterns related to robot vs. cursor control. The remaining class of neurons displayed overlapping activity patterns common to both conditions.

Our results suggest that switching between iBCI control manifolds is mediated by differential engagement of specific cortical sub-networks.

Keywords: Neural signal processing, Neural Signal Processing - Nonlinear analysis
Abstract: For brain study, synchronized neural signal have been analyzed with typical ways, but there is some need of new investigating approach. In this study, association rule learning was applied to simulated spike trains, and it was successful to discover strong rules corresponding to connections.

Keywords: Neural signal processing, Neural Signal Processing - Nonlinear analysis, Neural Signal Processing - Time frequency analysis
Abstract: For spike train analysis, burst detection is very important. Previous methods for detecting burst may not separate and detect all of the bursts because of its irregularity. In this research we suggest the method using less parameter and give a fuzzy result for intermediate criteria between burst and tonic.

Keywords: Neural signal processing, Brain Functional Imaging - EEG and Evoked Potentials
Abstract: This paper proposes an adaptive Kalman filtering based single trial evoked potential extraction method, which can automatically distinguish between evoked potential and noise. The results of simulation EEG and real EEG experiment show that this method obtains a favorable effect.

Keywords: Brain physiology and modeling, Human Performance - Sensory-motor, Brain Functional Imaging - EEG and Evoked Potentials
Abstract: In this paper we introduce finding on how brain areas interact when tempo changes in beat perception. The results of Independent Component Analysis and Dipole Localization on EEG recording showed that Motor Cortex and Auditory Cortex are both engaged in beat perception and the synchronization remained when the tempo changed..

Keywords: Neural signal processing, Brain-computer/machine Interface
Abstract: The relation between music and response emotions has been investigated for decades. The emotional states during music appreciation have been investigated. A Spearman correlation based electroencephalogram (EEG) feature analysis approach was proposed to capture emotion related neural oscillations. It achieved the average accuracy of 60.8% for the classification of high and low valence, and 63.9% for arousal. It was considered useful for building an affective BCI system.

Keywords: Brain-Computer/Machine Interface - Robotics applications, Brain-computer/machine Interface, Brain Functional Imaging - EEG and Evoked Potentials
Abstract: The aim of study is to design and implement a noninvasive brain-computer interface (BCI)-based brain-controlled robot. A 6-axis DENSO robotic arm was combined with a 15 targets steady-state visual evoked potential (SSVEP)-based BCI. During the experiment, all subjects were able to control the robot arm and completed the entire grasping task at their own will. The results demonstrated that the proposed SSVEP-BCI can provide accurate and robust control of the robotic arm.

Keywords: Neurological disorders - Stroke, Brain Functional Imaging - EEG and Evoked Potentials, Neural Interfaces - Sensors and body Interfaces
Abstract: Brain-computer interface is considered to be an alternative therapy for stroke rehabilitation. With this technique, patients are able to move the paralyzed hand with mind-control. However, those existed BCI systems can only provide an on-off control, which makes the movement types very limited. In this research, we propose a new strategy for two-dimensional control with the combination of motor imagery and motor attempt. As a result, the two-dimensional BCI decoding accuracy is 93.6% between lesioned and intact hands, while the averaged accuracy between MI and MA within one single hand is 85.4%. The results suggest MI and MA are physiologically different, and relevant cortical activations could be well discriminated to form a two-dimensional BCI for stroke rehabilitation.

Keywords: Neurological disorders - Stroke, Neurological disorders - Diagnostic and evaluation techniques, Neuromuscular Systems - EMG models, processing and applications
Abstract: This study compares neuromuscular changes post stroke between relatively proximal (biceps) and distal muscles (first dorsal interosscous (FDI) and thenar muscles) assessed by clustering index (CI) analysis of surface electromyography (EMG). The EMG data were recorded from three examined muscles of 12 stroke survivors and 8 age-matched healthy control subjects, respectively. It was unsurprising to observe diverse CI findings (neuropathy, myopathy or normality) between the proximal and distal muscles, whereas both distal muscles showed almost consistency. Our findings help to understand the specific pathological mechanisms underlying stroke sequela.

Keywords: Neurological disorders - Diagnostic and evaluation techniques, Neurological disorders - Stroke
Abstract: This study presents a novel application of sample entropy (SampEn) analysis of surface electromyography (EMG) in examining complex neuromuscular alternations following a hemispheric stroke. Compared to the healthy controls, dramatic variations in EMG SampEn were observed from paretic biceps muscles of 25 stroke survivors, indicating diverse and complex neuromuscular processes at work in the paretic muscle. Our findings help better understand mechanisms of stroke-induced paralysis and thus offer guidelines for a better design of targeted stroke rehabilitation protocol.

Keywords: Neural Signal Processing - Nonlinear analysis, Neurological disorders - Epilepsy
Abstract: Epilepsy is a common disease of brain disorder. Increment Entropy (IncrEn) is a new algorithm to measure the complexity of a time series. This research presents a method of detecting intracranial epileptic EEG using IncrEn and shows an appropriate choice of the parameters of IncrEn for analyzing epileptic EEG time series. The results demonstrated that IncrEn can effectively detect epileptic EEG using appropriate parameters of IncrEn and IncrEn was sensitive to the change of parameters for short data sets. We suggest that the value of the length of time-series should not less 100. At the same time, in order to make a clearly distinction on IncrEn value between interictal and ictal time-series, we recommend demension and quantifying resolution are taken between 2 and 4.