Keywords: Brain Functional Imaging - EEG and Evoked Potentials, Neural Interfaces - Computational modeling and simulation, Neural Interfaces - Neuroimaging
Abstract: Electroencephalography (EEG) signal has been playing a crucial role in clinical diagnosis and treatment of neurological diseases. However, it is very challenging to efficiently reconstruct the brain image given sources from very few scalp measurements due to high ill-posedness. Recently some efforts have been devoted to developing EEG source reconstruction methods using various forms of regularization, including the ell_1-norm, the total variation (TV), as well as the fractional-order TV. However, since high-dimensional data are very large, these methods are difficult to implement. In this paper, we propose accelerated methods for EEG source imaging based on the TV regularization and its variants. Since the gradient/fractional-order gradient operators have coordinate friendly structures, we apply the Chambolle-Pock and ARock algorithms, along with diagonal preconditioning. In our algorithms, the coordinates of primal and dual variables are updated in an asynchronously parallel fashion. A variety of experiments show that the proposed algorithms have more rapid convergence than the state-of-the-art methods and have the potential to achieve the real-time temporal resolution.

Keywords: Brain Functional Imaging - EEG and Evoked Potentials, Brain Functional Imaging - NIR, Neural signal processing
Abstract: Simultaneous collection of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) is a promising neuroimaging technique, which can provide high resolution in both spatial and temporal domain. Because EEG recorded in MRI scanners is heavily contaminated with gradient artefact (GA), removal of GA from EEG is a crucial step in EEG-fMRI data analysis. To date, the most efficient methods to remove GA are the average artefact subtraction (AAS) method and its extensions. However, these methods assume perfect synchronization between EEG and fMRI recording, which could be violated in practice. In this paper, a least across-segment variance (LASV) method is proposed for correcting EEG-fMRI desynchronization. Simulation and real data tests were conducted to check the performance of LASV method. The results suggested that the LASV method is able to efficiently correct EEG fMRI desynchronization in both synthetic and real data, providing a powerful tool for improving the performance of GA removal for desynchronized EEG-fMRI data.

Keywords: Brain Functional Imaging - EEG and Evoked Potentials, Brain Functional Imaging - MEG, Brain functional imaging
Abstract: To solve multiple comparisons problems in EEG/MEG analyses, cluster-based permutation test is possibly the most powerful approach, while it also inherits the advantage of well-controlled family-wise error rate from point-level permutation test. Because the cluster-level statistics used accumulate statistical power of all points in a cluster, cluster-based permutation test has a much higher sensitivity for widespread clusters. In this study, we demonstrate that, when the threshold for cluster inclusion is inappropriately set, the existence of larger clusters lowers the sensitivity for detecting the presence of smaller clusters, because the influence of large clusters on permutation distribution is overlooked in previous studies. Further, we demonstrated that increasing the threshold for cluster inclusion can efficiently solve this problem and then proposed a new guideline for threshold selection in the cluster-based permutation test. Results on simulated data and real data show the proposed guideline can greatly improve the sensitivity of cluster-based permutation test for detecting small clusters while retaining the same family-wise error rate.

Keywords: Brain Functional Imaging - Classification, spatiotemporal dynamics, Neural Interfaces - Neuroimaging, Brain Functional Imaging - Multimodal
Abstract: Accurate estimation of volumes for cerebrospinal fluid (CSF) and brain before and after surgery (pre-op and post-op) plays an important role in analyzing treatment for hydrocephalus. This in turn, relies upon segmentation of brain imagery into brain tissue and CSF. Segmentation of pre-op images is a relatively straightforward problem and has been well researched. However, segmenting post-op CT-scans becomes challenging due to distorted anatomy and subdural hematoma collections pressing on the brain. Most intensity and feature based segmentation methods fail to separate subdurals from brain and CSF as subdural geometry varies greatly across different patients and their intensity varies with time. We combat this problem by a learning approach that treats segmentation as supervised classification at the pixel level, i.e. a training set of CT scans with labeled pixel identities is employed. Inspired by sparsity constrained classification, our central contribution is a dictionary learning framework that learns class (segment) specific dictionaries that can efficiently represent test samples from the same class while poorly represent corresponding samples from other classes. Because discriminating features are discovered automatically, we call our method feature learning for image segmentation (FLIS). Experiments performed on infant CT brain images acquired from CURE children's hospital of Uganda reveal the success of our method against existing alternatives.

Keywords: Neurological disorders - Diagnostic and evaluation techniques, Neurological disorders - Epilepsy, Neural Interfaces - Neuroimaging
Abstract: Cavernous malformation or cavernoma is a kind of brain vessel abnormality that can cause serious symptoms such as seizures, intracerebral hemorrhage and various neurological deficits. It is one of the most common epileptogenic lesions that can be identified by physicians based on magnetic resonance imaging (MRI) of the brain. However, visual detection of cavernomas in a large set of brain MRI slices is a time-consuming task. This paper proposes a computer aided cavernomas detection method based on T2-weighted MRI analysis. The proposed method includes the following steps: template matching to find suspected cavernoma regions and classification based on support vector machines (SVMs) to remove most of the false positives. The performance of the proposed technique is evaluated and a sensitivity of 0.96 is obtained after testing.

Keywords: Human Performance - Fatigue, Human Performance - Drowsiness and microsleeps, Human Performance - Attention
Abstract: Attenuation of alpha wave is considered as the most valid marker of sleep onset during sleep, but this has received little attention during driving. Interestingly, from our simulated driving experiments, a new alpha wave's attenuation-disappearance phenomenon was observed to frequently appear in eye closure events (ECEs), with an obvious split point, which divides ECE into alpha attenuation phase and alpha disappearance phase. Firstly, we used box plots to visualize the general distribution of the alpha wave's power spectrum density (PSD) values in each of the two phases. Secondly, more quantitative analysis method was used to examine the characteristics of change in alpha PSD values. In addition, we analyzed the duration distribution of ECEs with alpha attenuation-disappearance phenomenon to measure sleepiness level and calculated the percentage of these ECEs across four sub duration ranges. The experimental results indicate that there is a general sharp decline of alpha PSD values from the alpha attenuation phase to the alpha disappearance phase among all the ten subjects. This result is consistent with visual observations and verified the alpha wave's attenuation-disappearance phenomenon as a general pattern. Moreover, this alpha attenuation pattern was proved to be more likely to appear under the condition of higher sleepiness level, predicting the entry into sleep.

Keywords: Neuromuscular Systems - EMG models, processing and applications, Motor learning, neural control, and neuromuscular systems
Abstract: Understanding how multiple muscles are recruited across a joint has typically been approached from relating muscle activity to resultant forces. New tools are now available to look deeper into a muscle’s recruitment by observing groups of single motor units. Identifying changes in the motor unit activity both within a muscle and between muscles the redundancies in motor control can be better understood. Motor units from biceps brachii and brachioradialis were decomposed from surface electromyography to determine firing rate and timing behaviors. Biceps peak firing rates, recruitment time and time to reach peak firing rate had positive correlation with increases in resultant force, while brachioradialis data had no such correlation. In addition the brachioradialis firing rate distribution was significantly different from the biceps. The stark differences in firing behavior show differences in function for both muscles, with the biceps acting more as a primary force producer, and the brachioradialis acting as a force modulator.

Keywords: Sensory Neuroprostheses - Visual, Sensory Neuroprostheses, Human Performance - Cognition
Abstract: The Argus II retinal prosthesis has a dissociation between the line of sight of the camera and that of the eye. The image-capturing camera is mounted on the glasses and therefore, eye movements do not influence the visual information sent to the implanted electrodes. We have demonstrated a closed-loop setup that shifts the visual information based on real-time eye position. In contrast to previous experiments, the setup does not require head restraints. The setup is based on a self-calibrating mobile eye tracker that allows free head movements. The patient was required to report the location of a white bar on a black background. An internal sensor was used to record the amount of head motion during the task. Results suggest that during combined eye-head scanning, head movement amplitude was significantly less than in the currently used head-only scanning. In the combined eye-head scanning, the patient first steers to the region of interest using eye movements followed by head movements as in sighted individuals. This is the first demonstration that eye movements can be used in combination with head movements to steer the line of sight of a camera-based retinal prosthesis.

Keywords: Human Performance - Fatigue, Neuromuscular Systems - Peripheral mechanisms, Motor learning, neural control, and neuromuscular systems
Abstract: It is of great significance to detect and predict muscle fatigue for avoiding muscle injury and the negative effects on human-machine interface. This paper presents a novel approach to obtain reliable information about muscle fatigue by analyzing surface electromyography (sEMG) and near-infrared spectroscopy (NIRS) simultaneously. Muscle fatigue was induced via sustained isometric contraction at 50% of maximal voluntary contraction (MVC) force with six subjects. Four fatigue metrics, root mean square (RMS) and median frequency (MDF) of sEMG, blood volume (BV) and muscle oxygenation (△HbO2) extracted from NIRS, were proposed to measure muscle fatigue from the perspective of electrophysiology and hemodynamics respectively. The experimental results suggested that sEMG and NIRS applied together could provide more detailed and reliable information about muscle fatigue, gaining a better understanding of fatigue process.

Keywords: Brain Functional Imaging - EEG and Evoked Potentials, Brain-Computer/Machine Interface - Biofeedback, Brain-computer/machine Interface
Abstract: Recently, SSVEP detection from EEG signals has attracted the interest of the research community, leading to a number of well-tailored methods. Among these methods, Canonical Correlation Analysis (CCA) along with several variants have gained the leadership. Despite their effectiveness, due to their strong dependence on the correct calculation of corre- lations, these methods may prove to be inadequate in front of potential deficiency in the number of channels used, the number of available trials or the duration of the acquired signals. In this paper, we propose the use of Subclass Marginal Fisher Analysis (SMFA) in order to overcome such problems. SMFA has the power to effectively learn discriminative features of poor signals, and this advantage is expected to offer the appropriate robustness needed in order to handle such deficiencies. In this context, we pinpoint the qualitative advantages of SMFA, and through a series of experiments we prove its superiority over the state-of-the-art in detecting SSVEPs from EEG signals acquired with limited resources.

Keywords: Brain Functional Imaging - Connectivity and Network, Brain Functional Imaging - Classification, spatiotemporal dynamics, Brain physiology and modeling
Abstract: We present here a browser-based application for visualizing patterns of connectivity in 3D stacked data matrices with large numbers of pairwise relations. Visualizing a connectivity matrix, looking for trends and patterns, and dynamically manipulating these values is a challenge for scientists from diverse fields, including neuroscience and genomics. In particular, high-dimensional neural data include those acquired via electroencephalography (EEG), electrocorticography (ECoG), magnetoencephalography (MEG), and functional MRI. Neural connectivity data contains multivariate attributes for each edge between different brain regions, which motivated our lightweight, open source, easy-to-use visualization tool for the exploration of these connectivity matrices to highlight connections of interest. Here we present a client-side, mobile-compatible visualization tool written entirely in HTML5/JavaScript that allows in-browser manipulation of user-defined files for exploration of brain connectivity. Visualizations can highlight different aspects of the data simultaneously across different dimensions. Input files are in JSON format, and custom Python scripts have been written to parse MATLAB or Python data files into JSON-loadable format. We demonstrate the analysis of connectivity data acquired via human ECoG recordings as a domain-specific implementation of our application. We envision applications for this interactive tool in fields seeking to visualize pairwise connectivity.

Keywords: Human Performance - Modelling and prediction, Brain-computer/machine Interface, Neural signal processing
Abstract: In Motor Imagery literature, performance predictors are commonly divided in four categories: personal, psychological, anatomical and neurophysiological. However these predictors are limited to inter-subjects changes. To overcome this limitation and evaluate intra-subjects performance, we tried to combine two groups of these measures: psychological and neurophysiological. As neurophysiological variables tonic changes in resting EEG theta and alpha sub-bands were considered. As psychological parameter we analyzed internalized attention and its correlates in lower alpha. We found that when internalized attention doesn’t decrease, Motor Imagery performance outcome can be correctly predicted by resting EEG tonic variations.

Keywords: Brain functional imaging, Clinical neurophysiology
Abstract: To study the metabolic changes following amputation, we investigated the metabolite concentrations in the secondary somatosensory cortex and thalamus from twenty-three upper limb amputees and sixteen age-matched healthy controls using proton magnetic resonance spectroscopy (MRS). The ratios of major metabolites to the creatine (Cr) peak were quantified to indicate metabolite levels. Compared with the healthy controls, the amputees showed significantly reduced NAA/Cr and Cho/Cr ratios in the secondary somatosensory cortex and thalamus contralateral to the amputation side, indicating neuronal loss and dysfunctions in these brain regions due to the loss of sensory input. Meanwhile, the stump sensation was found to be positively correlated with NAA/Cr ratio and negatively correlated with GSH/Cr ratio in the thalamus. The results suggested that both the thalamus and somatosensory cortex may undergo metabolic changes after amputation.

Keywords: Neuromuscular Systems - EMG models, processing and applications, Human Performance - Sensory-motor
Abstract: Three muscle activation estimators: a linear mean-absolute value filter, a recursive Bayesian method, and a kurtosis filter were compared as control approaches for an abstract myoelectric-controlled interface. The linear filter out performed both the Bayesian and kurtosis methods with respect to participants’ overall scores. Despite significantly less efficient trajectories, the Bayesian filter showed a reduction in the time required to reach individual targets. Results demonstrate both that linear methods can outperform more complex filtering techniques, and that real-time kurtosis may be used as an activation estimator.

Keywords: Human Performance - Cognition, Brain Stimulation - Transcanial magnetic stimulation (TMS)
Abstract: Although previous studies have shown that Broca’s area is closely related with word production, there is still a lack of consensus about the specific function of Broca’s area and in particular of its subparts, especially in Chinese Language. Here we used transcranial magnetic stimulation (TMS) to interfere with Broca’s area at different time window following picture onset to investigate the spatial and temporal characteristics of Broca’s area in the process of picture naming in Mandarin language. Performance was evaluated at baseline (sham TMS) and on line TMS. RTs were prolonged when TMS was applied over Broca’s area at 225ms time window, whereas sham stimulation and real stimulation at other time windows did not result in such changes in reaction time (RT). Taken together these findings is line with previous study that Broca’s area may be involved in the process of syllabification, but the exact time period might be somehow different due to ideographic feature in Chinese language.

Keywords: Brain Functional Imaging - Multimodal
Abstract: Monitoring hemodynamic responses in the rodent stroke model is important in the study of ischemic stroke. However, it is hard for single imaging modality to comprehensively explore these changes. In this study, we implemented laser speckle imaging (LSI) and visible-light optical coherence tomography (Vis-OCT) to detect cerebral blood flow (CBF), angiography and oxygen saturation (sO2) in the mouse model of distal middle cerebral artery occlusion (dMCAO). LSI provides full-field, real-time cerebral blood flow (CBF) map to guide the Vis-OCT imaging. Vis-OCT offers angiography and sO2 measurements. The results showed the detailed CBF and vasculature changes before and after dMCAO. After dMCAO, sO2 changed little in arteries and arterioles, while sO2 decreased in veins and venules. We also showed that larger veins had a higher decrease in sO2 value in consecutive vein branches after dMCAO. It is suggested that the combination of LSI/Vis-OCT provides a promising tool for the rodent stroke model study.

Keywords: Brain Functional Imaging - fMRI, Brain Functional Imaging - Connectivity and Network
Abstract: Major depressive disorder (MDD) is associated with excessive self-focus and a tendency to engage in self-reflection. Women are more prone to form negative thoughts when facing problems such as social rejection. The neuroimaging study specifically on MDD females is still rare. The medial prefrontal cortex, a central node of anterior default mode network (DMN), plays an important role in the pathophysiology of MDD. In this study, we explored the inter-regional correlation feature of mPFC with whole brain through functional connectivity (FC) analysis of resting state functional magnetic resonance imaging (rs-fMRI) data. Increased FC with left anterior cingulate cortex (ACC_L) was found in female MDD patients compared with healthy controls. Furthermore, the FC strength of ACC_L was positively correlated with clinical scores, which implied the important role of mPFC-ACC pathway in MDD females. Moreover, decreased FC with right middle temporal gyrus (mTG_R) and left middle temporal gyrus (mTG_L) was found in MDD females, which may result from the low self-evaluation in MDD. This FC analysis may help us better understand the mechanism of MDD.

Keywords: Neurological disorders - Diagnostic and evaluation techniques, Neuromuscular Systems - Wearable systems, Neuromuscular Systems - Locomotion, posture and balance
Abstract: Quantitative assessment of the motor symptoms in Parkinson's disease is the key to early treatment and essential for long term management over years. Wearable devices provide a new approach to quantify the motor symptoms in Parkinson's disease. Monitoring of the symptoms at multi-joints and body, on-line monitoring, quick analysis, low load and less restriction to the patients raise challenges to develop proper wearable device for patients monitoring in hospital. In this paper, multi-sensor wearable devices were developed to monitor and quantify the movement symptoms in Parkinson's disease. Five wearable sensors were used to record motion signals from patient’s bilateral forearms, legs and waist. A local area network based on low power Wi-Fi technology was built for wide range and long period wireless data transmission. A software was developed for motion signal recording and analyzing. The size of each sensors was 52 mm×37 mm×13 mm and the weight was 26.3 g. The sensors were rechargeable and able to run 13 hours per charge. The wireless transmission radius was over 40 m. The wearable devices were tested in patients and normal subjects. The devices were reliable and with good temporal resolution and spatial resolution for movement monitoring in hospital.

Keywords: Human Performance - Fatigue
Abstract: Physiological signals such as EEG and EOG have been successfully applied to detect driving fatigue in single modality. In this paper, we propose a multimodal approach by combining partial EEG and forehead EOG to enhance driving fatigue detection. We investigate the key brain area where we collect the EEG to combine with forehead EOG. Our experiment results demonstrate that the temporal EEG signals from six-channel have the best performance when combining with forehead EOG to extract shared features. Furthermore, we propose a novel multimodal fusion strategy using deep autoencoder model to learn a better shared representation. We assess our approach with other fusion strategies on 21 subjects. Our multimodal approach achieves the best performance that the average COR and RMSE are 0.85 and 0.09, respectively. The experiment results demonstrate that our multimodal approach could learn an efficient shared representation between modalities and could significantly improve the performance of detecting driving fatigue.

Keywords: Neuromuscular Systems - EMG models, processing and applications, Neuromuscular Systems - Wearable systems
Abstract: Recently the number of people with swallowing disorders (dysphagia) is increasing. Therefore, efforts are needed to support the swallowing process of dysphagia patients. In order to assist the swallowing process, understanding the initial phase is important. As the first step of development, we analyzed muscle activities related to the initial phase of the swallowing process. In this research, muscle activities were measured using surface electromyography (EMG) and swallowing activities were recorded with videofluoroscopy (VF). From this analysis, we found two peaks in the EMG signals of suprahyoid muscles. Masseter muscle activity was also registered before the first peak of the suprahyoid muscle’s activity. By comparing between the VF and the EMG waveforms, we concluded that the activities of those muscles point to the initial phase of the swallowing process. Finally, we developed a model to estimate the initial phase of the swallowing process based on the results.

Keywords: Neurological disorders - Diagnostic and evaluation techniques, Neuromuscular Systems - Locomotion, posture and balance
Abstract: Parkinson's disease (PD) is a neurodegenerative disorder and severely affects their daily life. The diagnosis depends on the presence of one or more of common motor symptoms of the disease. As PD is chronic and slowly progressive, the symptoms continue and worsen over a long period of time. Quantification of the motor symptoms of PD is helpful for diagnosing and monitoring patients’ conditions. This paper provided approaches to quantify the motor symptoms of Parkinson’s disease with wearable devices, and the quantification was developed with four paradigm maneuvers. The quantification parameters included tremor frequency, amplitude of hands movement, speed of foot-tapping and turning duration. The approach provided quantitative measures for the tremor, bradykinesia and gait symptoms, which could be useful for optimization of drug or deep brain stimulation treatments.

Keywords: Human Performance - Attention
Abstract: Attention of subjects in EEG-based emotion recognition experiments determines the quality of EEG data. Traditionally, self-assessment with questionnaires is used to evaluate the attention degree of subjects in experiments. However, this kind of self-assessment approach is subjective and inaccurate. Low quality EEG data from subjects without attention might influence the experiment evaluation and degrade the performance of affective models. In this paper, we extract scanpaths of subjects while watching emotion clips with eye tracking glasses and propose an attention evaluation method with spacial-temporal scanpath analysis. Based on the assumption that subjects with attention have similar scanpath patterns under the same clips, our approach clusters these similar scanpath patterns and evaluate the attention degree. Experimental results demonstrate that our proposed approach can cluster EEG features under attentive conditions effectively and significantly improve the classification performance. The mean accuracy of emotion recognition based on clustered high quality data is 81.70%, whereas the mean accuracy of using the whole dataset is 68.54%.

Keywords: Neuromuscular Systems - EMG models, processing and applications, Neuromuscular Systems - Computational modeling and simulation, Neural Signal Processing - Blind source separation
Abstract: We assessed the impact of different motor unit action potential (MUAP) components in dynamic muscle contractions on decomposition of high-density surface electromyograms (hdEMG). In particular, hypothesis that nontravelling MUAP components, originating from the tendon regions, are less sensitive to changes in geometry of fusiform muscles than travelling MUAP components has been tested on synthetic monopolar hdEMG signals. The latter have been decomposed by previously introduced Convolution Kernel Compensation (CKC) method, using five different sections of simulated MUAPs for motor unit identification. Accuracy of decomposition results increased significantly when motor units were identified from the nontravelling MUAP components, compared to the results obtained from travelling components. Average motor unit identification sensitivity increased from 67.4%±15.7% to 81.3%±11.3% and false alarm rate decreased from 0.75% ± 1.21% to 0.20% ± 0.24%. Results confirmed that non-travelling MUAP components are discriminative enough to reliably identify motor units from hdEMG and less sensitive to geometric changes of fusiform muscles during dynamic muscle contractions than travelling MUAP components.

Keywords: Human Performance - Cognition
Abstract: This paper aims to explore the neural patterns between Chinese and Germans for electroencephalogram (EEG)-based emotion recognition. Both Chinese and German subjects, wearing electrode caps, watched video stimuli that triggered positive, neutral, and negative emotions. Two emotion classifiers are trained on Chinese EEG data and German EEG data, respectively. The experiment results indicate that: a) German neural patterns are basically in accordance with Chinese ones; b) the main difference lies in the upper temporal region in Delta band which activates more when a German is in positive mood; and c) the Chinese positive emotion achieves the best accuracy while German emotions share the approximate accuracy. Moreover, Gamma band serves as the critical band for both German and Chinese emotion recognition.

Keywords: Neuromuscular Systems - Wearable systems, Motor Neuroprostheses - Robotics, Neural Interfaces - Sensors and body Interfaces
Abstract: Active lower limb exoskeletons can provide assistance to the lower extremities and may drastically improve the walking abilities of millions of individuals with gait impairments. However, most currently available control systems for these devices cannot predict the user’s intended movements and have yet to enable walking with seamless transitions. Recent developments in intent recognition for active lower limb prostheses have demonstrated that using kinematic and kinetic signals from the device and myoelectric signals from the user can provide an intuitive control interface for seamlessly transitioning between different locomotor activities. In this work, we determined the baseline performance of intent recognition systems using neuromechanical signals presumably accessible for controlling active lower limb exoskeletons. We collected bilateral lower limb joint kinematics and muscle activity from three able-bodied subjects while they walked on level ground, ramps, and stairs in order to train an intent recognition system. We found that both combining kinematic and myoelectric signals and including signals from the contralateral leg significantly improved intent recognition performance. We achieved an average offline prediction error rate of 1.4 ± 0.90% using bilateral kinematic and myoelectric signals, demonstrating the promising potential of translating prosthesis-based intent recognition as an alternative control strategy for active lower limb exoskeletons.

Keywords: Brain Functional Imaging - Connectivity and Network, Neural Signal Processing - Nonlinear analysis, Neural Signal Processing - Blind source separation
Abstract: Functional intrinsic brain networks (IBNs) has been widely studied due to its close relationship to different brain functions and diseases. In these studies, linear metrics, e.g., correlation, have been commonly used in identifying brain networks, especially on functional magnetic resonance imaging (fMRI) data. However, nonlinear mechanism is believed to exist in forming brain networks. In the present study, we investigated the performance of a nonlinear metric, i.e., phase coherence, in probing brain networks, as compared with a linear metric, i.e., power correlation. Specifically, individual IBNs were firstly obtained by a time-frequency independent component analysis (tfICA), and then the interaction among them were probed using either phase coherence (inter-component phase coherence, ICPC) or power correlation coefficient (PCC). We examined them using high-density resting-state electroencephalography (EEG) data from a group of patients with a balance disorder who received repetitive transcranial magnetic stimulation (rTMS) treatments. The results indicated that the use of ICPC indicated more detections of significant connectivity crossing multiple brain regions in various frequency bands than PCC. Moreover, consistent treatment-related network changes, as compared with previous neuroimaging findings, in this brain disorder were more successfully detected with ICPC. Therefore, it is important to use nonlinear metric in characterizing interactions between different brain re

Keywords: Human Performance - Attention, Human Performance - Cognition, Human Performance - Modelling and prediction
Abstract: Qualitative motion analysis with motion magnification techniques opened a new dimension to videos by revealing imperceptible information hidden in small movements. Those movements can contain information on neurocognitive and affective states and thus could be a novel source of information during driving or neurocognitive experiments, complementing secondary sensors in the respective setup.

While motion magnification techniques are tools for visualization of small movements, dense optical flow can quantify those movements with sub-pixel precision.

In this work, we discuss how quantitative motion analysis with dense optical flow fields from video recordings can be used for online non-contact affective and neurocognitive assessment.

Keywords: Brain Functional Imaging - NIR, Neurological disorders - Psychiatric disorders, Human Performance - Cognition
Abstract: Schizophrenia is a severe psychiatric disorder that affects individuals across the course of their lifetime. Neurocognitive deficits are a typical characteristic of this disorder and are prominent in many cognitive domains including attention and fluency. Despite the recognized importance, patient cognition is rarely monitored as a regular part of treatment and even then, only with behavioral metrics. Functional Near Infrared Spectroscopy (fNIRS) is a non-invasive technique which measures cortical hemodynamic responses using near-infrared light with safe, portable, and cost-effective sensors. The measurement of fNIRS during cognitive processing tasks could offer objective insights into the dysfunction and cognitive inefficiency inherent in Schizophrenia. In this preliminary study, we examined cognition in patients with Schizophrenia and Controls under three different cognitive tasks (Continuous Performance Test, Verbal Fluency Test, Psychomotor Vigilance Test) to evaluate differences in behavioral and candidate biomarkers in the disorder. Our results support findings of left-hemispheric lateralization during language tasks, right-hemispheric lateralization during attention tasks, and imply task-related abnormal activation patterns in schizophrenia patients.

Keywords: Brain Functional Imaging - Connectivity and Network
Abstract: Abstract—Being the common type of epilepsy, the generalized tonic-clonic seizure is characterized by specific clinical symptoms, such as the loss of consciousness, physical convulsions, orthotonus and then clonicity. In the present study, the adaptive directed transfer function was applied to probe the dynamic brain network topological changes before, during and after the neurons’ abnormal spiking. Findings showed that the brain network topological changes were closely related to the neurons’ abnormal spiking, and also revealed the transferring of the brain activation to the epilepsy focus.

Keywords: Human Performance - Sensory-motor, Brain Functional Imaging - EEG and Evoked Potentials, Brain-computer/machine Interface
Abstract: Electroencephalography (EEG) and brain-computer interfaces (BCI) are receiving increasing attention and expanding application in stroke study. To identify stroke patients and normal controls during mental rotation task, common spatial pattern (CSP) algorithm is employed to extract features from binary-class EEG which will be further to form the dictionary for sparse representation. In the classification process, sparse representation-based classification (SRC) method is used; specifically, each test trial is sparsely represented over the formed dictionary and the sparse coefficient is obtained by solving a l1-norm regularized least squares minimization objective. A series of experiments demonstrated the effectiveness of features extracted by CSP of both classes and the SRC could obtain excellent results in classification. These results suggest that stroke patients have distinct EEG feature from normal controls. These EEG features may be potential biomarkers to monitor the rehabilitation process of stroke patients.

Keywords: Human Performance - Modelling and prediction, Human Performance - Sensory-motor, Motor learning, neural control, and neuromuscular systems
Abstract: In this study, we propose a novel method to explore the relationship between grip force and forearm muscle thickness during isometric contraction. One-directional ultrasound was used to detect the depth of muscle–bone interface. By tracking the movement of this interface, the change of muscle thickness can be calculated. A dynamometer was used to measure force. Three able–bodied subjects were asked to gradually increase grasping force from 0% to 40% maximum voluntary contraction (MVC) and decrease grasping force from 40% to 0% MVC following the guiding patterns on the screen, 4 channels of one-directional ultrasound signals on the forearm were recorded in the meanwhile. The relationship between extensor digitorum muscle and grip force was analyzed and the results showed that the relationship between this muscle and grip force can be represented with two exponential functions, with averaged R2 value of 0.982. And the existence of hysteresis in the muscle thickness-force relationship is discovered. These relationships have potential importance on grip force estimation.

Keywords: Human Performance - Fatigue, Neural Interfaces - Sensors and body Interfaces, Human Performance - Modelling and prediction
Abstract: In this paper, the thickness change of biceps brachii during the fatigue process is continuously monitored using one micro-size single-element ultrasound transducer and analyzed by the echo tracking algorithm. We focus on isometric, 50 % maximum voluntary contraction (MVC) of the elbow. Four subjects participates in the experiment and the result shows that the muscle thickness of biceps brachii continues to increase during the fatigue. There are two stages of the increasing process, the first of which sees a much higher increasing speed of the muscle thickness. Curve fitting using exponential functions is applied and the fitting result turns out to be good with the R^2 of 0.96. The preliminary study reveals that those custom-made single-element transducers would effectively extract muscle information to assess muscle fatigue.

Keywords: Human Performance - Modelling and prediction, Neural signal processing, Neural Interfaces - Neuroimaging
Abstract: This paper presents an original framework based on deep learning and preference learning to retrieve and characterize biomedical images for assisting physicians in diagnosing complex diseases with potentially only small differences between them. In particular, we use deep learning to extract the high-level and compact features for biomedical images. In contrast to the traditional biomedical algorithms or general image retrieval systems that only consider the use of pixel and/or hand-crafted features to represent images, we utilize deep neural networks for feature discovery of biomedical images. Moreover, in order to be able to index the similarly referenced images, we introduce preference learning in a novel way to learn what kinds of images we need so that we can obtain the similarity ranking list of biomedical images. We evaluate the performance of our system in detailed experiments over the well-known available OASIS-MRI database for whole brain neuroimaging as a benchmark and compare it with those of the traditional biomedical and general image retrieval approaches. Our proposed system exhibits an outstanding retrieval ability and efficiency for biomedical image applications.

Keywords: Neuromuscular Systems - Neurorehabilitation, Neural Signal Processing - Nonlinear analysis, Neurorehabilitation - Robotics
Abstract: This work describes the application of nonlinear kernel based regression method for prediction of 3 degrees of freedom (DoF) wrist movement from surface electromyogram (sEMG) signals. Here, two semicircular motion trajectories have been used, where different DoFs are engaged in a free hand movement and near the both extremities of the motion profile all the three DoFs of wrist are involved. Compared to earlier studies this is significant in the fact that the motion profiles arewith free and unconstrained hand movement and are closely related to the real life complex movement situations. The proposed method involves learning the wrist motion profile of a subject from a known data set comprising of motion sensor, sEMG and then tries to predict the motion profile from a new and unknown dataset of sEMG only from the same subject. Prediction accuracies for data in cross validation stage and for new data are presented. Though the accuracy of prediction is poorer in the latter case since we are considering conditions of free hand movements as in practical situations the result shown is quitesignificant considering the focus of developing independent, proportional and simultaneous control of multiple DoFs of prosthesis.

Keywords: Brain Functional Imaging - EEG and Evoked Potentials, Brain-computer/machine Interface, Brain Functional Imaging - NIR
Abstract: Hybrid Brain Computer Interfaces (BCI) has shown great promise for neuro-prosthetics and assistive devices in rehabilitation. However, the complexity of the BCI system and time cost for classification of motor tasks limit its applications. To overcome these changelings, concurrent Electroencephalography (EEG) and functional Near-Infrared Spectroscopy (fNIRS) recording approach was proposed in this study and tested on three healthy volunteers with a left-right hand grasping paradigm. A wavelet-based method was employed to extract the wavelet approximation coefficients from EEG signals and the slope information was employed to discriminate the concentration change of Oxygenated hemoglobin (HbO) during left-right hand grasping tasks. To maximize the valuable information carried in the two modalities, we proposed an approach based on principle component analysis (PCA) to integrate the features of fNIRS and EEG signals. Two classifiers, including support vector machine (SVM) and linear discriminant analysis (LDA) were applied to identify and estimate the control signals associated with left-right hand grasping tasks. The present experimental result demonstrates that the complement of EEG and fNIRS can significantly improve the classification accuracy by 3~9% on average. The reduction of dimensionality by PCA could achieve a reduction of time complexity and computational complexity with little loss in accuracy.

Keywords: Brain Functional Imaging - EEG and Evoked Potentials
Abstract: Node similarity refers to the link probability in complex network. Higher value signifies their strong possibilities for interlinkages. In this paper, four techniques were tested for node similarity evaluation including CN (common neighbor), RA (resource allocation), AA (Adamic-Adar) and Sorenson algorithm on several real and simulated networks. AUC (Area Under the Curve) was set to compare their prediction accuracy. It’s found out that RA performs much better and then it was adopted on frequency dependent brain networks with generalized lobe epilepsy disease. Similarity matrix was produced and the results demonstrated that RA could help in uncovering pathological mechanism through leading nodes identification based on node similarity.

Keywords: Brain Functional Imaging - MEG, Brain Functional Imaging - Source localization, Brain Functional Imaging - EEG and Evoked Potentials
Abstract: We present novel hierarchical multiscale Bayesian algorithms for electromagnetic brain imaging using magnetoencephalography (MEG) and electroencephalography (EEG). We define sensor data measurements using a generative probabilistic graphical model that is hierarchical across spatial scales of brain regions and voxels. We then derive Bayesian algorithms for probabilistic inference with this graphical model. Performance of algorithm shows superiority to standard benchmark algorithms and more novel algorithms both in simulations and with real data.

Keywords: Neuromuscular Systems - Neurorehabilitation, Neurorehabilitation - Wearable systems, Neurological disorders - Stroke
Abstract: The Helping Hand (HH) system is a novel grasp rehabilitation platform aimed at simplifying the clinical usage of wearable electrode arrays for neuromuscular electrical stimulation (NMES). In a randomized dose-matched, clinical study we evaluate usability and effectiveness of the HH treatment, and of other enriched upper limb rehabilitation treatments, and compare the outcomes. This paper shows the preliminary clinical results of the trial on 5 chronic stroke patients throughout a 9 weeks, 3 hours per week, hand preshaping training.

Keywords: Neurological disorders, Brain Functional Imaging - Classification, spatiotemporal dynamics, Brain Functional Imaging - Connectivity and Network
Abstract: Much effort has been made to find neuroimaging markers for diagnosis of vascular dementia (VaD). In this paper, we combined EEG and support vector machine (SVM) to differentiate early VaD patients from controls. Interregional directed connectivity and resulting topological parameters were extracted and entered into classification respectively. It was found that VaD patients can be correctly separated from controls with high accuracy. A large proportion of discriminant features were associated with parietal and temporal regions. This was highly consistent with those obtained through traditional statistical tests. The study may facilitate automatic VaD diagnosis at an early stage in future clinical practice.

Keywords: Brain Functional Imaging - EEG and Evoked Potentials
Abstract: EEG is extensively used to study the mechanism of decline in brain function during aging. In this paper, we compared typical indexes of EEG microstate between normal elderly group and youth group in resting state, i.e. amplitude, duration, frequency, coverage, to investigate the effect of normal aging on the brain function. Significant differences were found between these two groups. The study may provide an important reference for the research of brain aging and neurodegenerative diseases in the future.

Keywords: Brain Functional Imaging - EEG and Evoked Potentials, Brain Functional Imaging - Connectivity and Network
Abstract: We implemented an EEG-based hyperscanning system that can simultaneously acquire EEG data from 20 human subjects. Through two preliminary experiments, we validated the feasibility of our massive hyperscanning system and its potential usability as a new tool for social neuroscience.

Keywords: Brain Functional Imaging - EEG and Evoked Potentials, Brain Functional Imaging - Connectivity and Network, Brain Functional Imaging - Source localization
Abstract: This study aimed to investigate whether individuals with childhood trauma show impaired cortical functional connectivity network during response inhibition task. The network indices were impaired in high childhood trauma questionnaire group for low-beta frequency band. The small-world functional network is disrupted in participants with childhood trauma.

Keywords: Human Performance - Cognition, Neurorehabilitation - Wearable systems
Abstract: The purpose of this basic study is using bio-signal to find dif-ficulty factors for the Korean language learners during the lis-tening processing. Three types of bio-signal; electroencephalography (EEG), galvanic skin response (GSR) and heart rate were measured from the subject when doing a professional Korean listening test. Meanwhile, a language supervisor recorded the question answer from linguistic term to observe the subjects. Finally, the acquired bio-signals was compared with the recorded results by supervisor that was used to analysis the real difficulty factors of the learners in the Korean listening process.

Keywords: Brain Functional Imaging - NIR, Neural Interfaces - Neural microsystems and Interface engineering
Abstract: This paper presents a post layout simulation results of a 117 dBΩ gain, 20 MHz bandwidth and 7.2 nArms total integrated input referred noise optical receiver for the Near Infrared Spectroscopy (NIRS). The chip is optimized for long battery life time operation with a 2.35 mW total power consumption. An Automatic Gain Control (AGC) is presented to extend the maximum overloading input current of the chip. The chip can operate at an input current of 6.4 μAp-p with less than 1% Total Harmonic Distortion (THD) at the output. The optical receiver is optimized for 2 pF off-chip photodiode.

Keywords: Brain Functional Imaging - MEG, Brain Functional Imaging - EEG and Evoked Potentials, Brain Functional Imaging - Classification, spatiotemporal dynamics
Abstract: Tension-type headache (TTH) is defined by bilateral pressing or tightening pain over the head or neck. To unveil the underlying mechanism of TTH and its chronification, we used magnetiencephalographic (MEG) recording to investigate the cortical responses to paired-pulse electrical stimulation paradigm in patients with chronic TTH (CTTH), episodic TTH (ETTH) and in healthy controls. Larger amplitudes in the contralateral primary somatosensory cortex (cSI) for first and second stimulation were observed in CTTH than in controls, and larger gating ratios (ampitude to second stimuli/amplitude to first stimuli) in cSI were noted in CTTH and ETTH than in controls. This MEG study demonstrated that CTTH was characterized with the hyperexcitability and disinhibition in cSI, whereas deficiency of inhibitory function was also observed in ETTH.

Keywords: Neural Interfaces - Computational modeling and simulation, Sensory Neuroprostheses - Visual, Neural Interfaces - Neural stimulation
Abstract: A morphologically and electrophysiologically detailed computational alpha ganglion cell model has been created and validated using newly acquired experimental data. The ganglion cell model was integrated into a coupled electromagnetic-neuronal dynamics model of the experimental setup, providing validation for the simulation platform. Additionally insights into the stimulation electrode position dependent stimulation mechanisms have been gained.

Keywords: Neurological disorders - Epilepsy, Neural signal processing, Brain Functional Imaging - Classification, spatiotemporal dynamics
Abstract: This study introduces a new method for separating HFOs from background artifacts based on signal rhythmicity. EEMD is first applied on SEEG to extract IMFs, then amplitude and rhythmicity thresholds are used to extract HFOs from high frequency IMFs. The rhythmicity is represented by instant phase acceleration obtained from Hilbert transform. The results show that proposed method can achieve reliable HFOs extraction in ictal SEEG from five epileptic patients. The HFOs analysis also provides meaningful information.

Keywords: Human Performance - Cognition, Brain Functional Imaging - fMRI
Abstract: Perceptual decisions can be biased by a propensity to choose one stimulus alternative over another. Such bias is revealed especially in cases when there is inadequate stimulus evidence. Here we report on a three-alternative forced choice (3-AFC, Face vs. Car vs. House) visual categorization task, where subjects, on average, exhibited a “face bias”: faster response times for faces than for cars, even when degraded (low stimulus evidence) images were presented. We used simultaneous EEG and fMRI to characterize the neural correlates underlying this face bias. Specifically, in the first-level EEG-informed fMRI analysis, we incorporated EEG trial-to-trial variability to temporally identify a brain network related to a late decision process. To link the individual differences in bias with the changes in BOLD activity in the decision network, we used the subject-wise bias, obtained by fitting the subject’s behavioral data using the drift diffusion model, as a covariate in the group-level fMRI analysis. Our results showed significant correlation between decision bias and activity in a distributed occipito-parieto-frontal network. Our work shows that some decision related variables may be represented in a distributed fashion across regions in the human brain.

Keywords: Brain Functional Imaging - Source localization, Neurological disorders - Epilepsy, Brain physiology and modeling - Neural dynamics and computation
Abstract: Accurate localization of target points for direct stimulation have the potential to improve successful application of neuro-stimulation for the treatment of epilepsy. Previous studies showed, that sLORETA based on intracranial grid recording can successfully localize the maximum of a generator but exhibits a blurred activation volume. This study shows, on the basis of realistic simulations by a spatiotemporal computational model, that by restricting the source space to the gray matter ribbon more realistic estimates of the size of extended sources can be achieved.

Keywords: Brain physiology and modeling - Neuron modeling and simulation, Brain Stimulation - Transcranial direct current Stimulation (tDCS), Brain Stimulation - Transcanial magnetic stimulation (TMS)
Abstract: Coupled electromagnetic-neuronal dynamics modeling of transcranial electric and magnetic stimulation has been performed within a detailed anatomical head model featuring ~30’000 morphologically and electrophysiologically detailed pyramidal neuron models. Considering neuronal response is found to be important for the prediction of suprathreshold stimulation, while subthreshold polarizability correlates well with the normal component of the electric field. Neuron morphology and orientation has an important impact on excitability and latency that could be exploited to achieve better selectivity.

Keywords: Brain Functional Imaging - NIR, Human Performance - Gait, Neuromuscular Systems - Locomotion, posture and balance
Abstract: Interactive treadmill was developed to solve the conventional treadmill problem. In this study, functional near-infrared spectroscopy (fNIRS) was utilized to evaluate activation frontoparietal lobe of the brain during both treadmills walking. Twenty healthy subjects participated in the experiment and two kinds of desired walking speeds (slow/fast) were provided. The results showed that in both speeds PFC cortical activation was additionally facilitated under interactive treadmill, and oxyHb concentration on the interactive treadmill use was increased at fast speed.

Keywords: Neuromuscular Systems - EMG models, processing and applications
Abstract: In the experiment, 10 healthy college-aged individuals (five males, five females, mean age 22 years old) volunteered to participate in the study. They were asked to perform 5 different grip force control tasks. During the experiment, grip force signal and electromyography (EMG) signals were recorded simultaneously. To explore the relationship between grip force and EMG signals in responds to different task demands from an entropy point of view, we utilized fuzzy approximate entropy (fApEn) to reflect the complexity of grip force and EMG signals in this study. The results showed that the fApEn of kinetics decreased when the target was more difficult, while the fApEn of EMG signals increased.

Keywords: Human Performance - Modelling and prediction
Abstract: The accuracy of human intention prediction in target reaching motion was compared for various combinations of velocity, position, force and EMG signals by using autoregressive (AR) and Radial basis function neural network (RBFNN) model. From the analysis, we found that for simple tasks the accuracy of intention prediction is higher when velocity and position signals are used.

Keywords: Brain functional imaging, Brain Functional Imaging - Connectivity and Network, Neurorehabilitation - Neurofeedback
Abstract: This case study showed the effect of neurofeedback gait training (NGT) in a patient with stroke. The patient received intervention with or without NGT. After fNIRS recordings, functional connectivity evaluated the effect of NGT on motor recovery. This study demonstrated significant improvements in the motor-related cortical regions including M1, SMA, and PMC, and also noticeable enhancement in the PFC which controls the attention and motivation. The conclusion is that the use of NGT for a person with stroke can spread to be applied to stroke survivors.

Keywords: Neuromuscular Systems - EMG models, processing and applications
Abstract: Four kinds of upper limb tracking tasks with increasing complexity were performed to study the changes of kinematics and electromyography (EMG) signals towards different task demands. Results showed the entropy of elbow angle increased when tracking became more difficult while it was the opposite for biceps EMG signals. Entropy analysis might reveal the mechanisms of human motor control in neuromuscular system.

Keywords: Human Performance - Cognition, Brain Functional Imaging - NIR, Human performance
Abstract: For elucidating the mechanism of brain activations with the maternal five senses, we propose investigating the relationship between cognitive functions and prefrontal hemodynamics in a women’s brain after childbirth.

Keywords: Brain Functional Imaging - MEG, Brain Functional Imaging - Classification, spatiotemporal dynamics, Brain-computer/machine Interface
Abstract: This project explored the possibility to decode spoken phrases from non-invasive brain activity (MEG) signals. We used dynamic time warping and Wiener filtering for noise reduction, Gamma band filtering for feature extraction, and then Gaussian mixture model and artificial neural network as the decoders. Preliminary results demonstrated the possibility of decoding speech production directly from MEG signals.

Keywords: Human Performance - Cognition, Neurorehabilitation
Abstract: We strongly suggest that supraliminal control with phosphene as pseudo-blindsight might be an interesting technique for recovery from lost visual functions. In addition, it is perfectly painless.

Keywords: Brain Functional Imaging - Connectivity and Network, Brain Functional Imaging - EEG and Evoked Potentials, Brain Functional Imaging - Source localization
Abstract: Resting state networks (RSNs) has been extensively studied using functional magnetic resonance imaging (fMRI). Recently, novel methods to study RSNs using electroencephalography (EEG) have also been developed. In these studies, independent component analysis (ICA) is an important step in deriving RSNs and it can be applied to sensor space or source space data obtained through inverse source imaging techniques. Differences have been observed in results via both techniques even using same data. In the present study, we compared the sensor-space ICA and source-space ICA using simulated and real resting-state EEG data. The present results revealed that the source-space ICA has better performance on reconstructing both spatial and temporal features of RSNs.

Keywords: Brain Functional Imaging - MEG, Neural Interfaces - Neural stimulation
Abstract: After amputation, the somatosensory cortex corresponding to lost fingers might be invaded by other cortical areas due to cerebral plasticity. However, it was seldom observed how the amputees could feel the evoked tactile sensation in the cerebral cortex under transcutaneous electrical nerve stimulation (TENS). By using magnetoencephalography (MEG), we investigated the cortical response under TENS of the evoked thumb territory and normal thumb. It was found that the maximum positive peak latencies of the event-related magnetic field were 60 ± 1.41 ms for the evoked thumb and 46 ± 1.25 ms for the normal counterpart. The strongest Equivalent Current Dipole (ECD) corresponding to evoked thumb was located in the central sulcus. And the lost-finger tactile sensation via TENS of the evoked finger territories was produced in the cerebral cortex near the mirror location of the normal thumb counterpart.

Keywords: Brain Functional Imaging - fMRI
Abstract: In this paper, resting-state functional magnetic resonance imaging (R-fMRI) data of 15 patients with cerebral infarction were recorded first. Then, Pearson's correlation based hierarchical clustering (PCHC) method and independent component analysis (ICA) were performed to analyze and extract multiple correlation patterns. Finally, voxel-based aggregation index (VBAI) was used to quantitatively compare this two results and indicate that PCHC method has higher sensitivity than ICA.

Keywords: Neuromuscular Systems - EMG models, processing and applications
Abstract: To realize accurate muscle force estimation, an isometric muscle force estimation framework based on high-density surface EMG Array and NMF algorithm, which can extract the input of the prediction model from the appropriate activation area of the skeletal muscle, was proposed in this paper. The proposed method could significantly improve the quality of force estimation compared with conventional methods, which thus can be further employed in muscle heterogeneity analysis, myoelectric prostheses and the control of exoskeleton devices.

Keywords: Human Performance - Gait
Abstract: Combining with the motion stability, intensity and complexity of trunk and limbs, this study conducted an investigation on the inter-limb coordination pattern choice under different crawling speeds, and the research results provide the evidence that CNS choose stable inter-limb coordination pattern to keep body safe and avoid the tumble during crawling movement.

Keywords: Neuromuscular Systems - EMG models, processing and applications
Abstract: This study aims to explore the neuromuscular control mechanism of human hands-and-knees crawling by means of muscle synergy analysis. A good consistency of muscle synergy structure through different crawling speeds was found, and the recruitment level, duration, and phase of muscle synergies were adjusted to adapt the change of speed. The research results could be well explained by a two-level central pattern generator (CPG) model consisting of a half-center rhythm generator (RG) and a pattern formation (PF) circuit.

Keywords: Neuromuscular Systems - Wearable systems, Neurological disorders - Diagnostic and evaluation techniques, Neural signal processing
Abstract: This study proposes a novel framework for quantitatively evaluating upper limb motor function based on data fusion of inertial measurement units (IMUs) and surface electromyography (EMG) sensors. It offers a feasible solution to motor function assessment in an objective and quantitative manner, especially suitable for home and community use.

Keywords: Neural Interfaces - Computational modeling and simulation, Sensory Neuroprostheses, Neural signal processing
Abstract:

Keywords: Neural Interfaces - Computational modeling and simulation, Neural signal processing, Neuromuscular Systems - Computational modeling and simulation
Abstract:

Keywords: Brain Stimulation-Deep brain stimulation, Neurological disorders, Neural signal processing
Abstract:

Keywords: Neural Interfaces - Biomaterials, Neural Interfaces - Regeneration and tissue-electrode Interface
Abstract:

Keywords: Sensory Neuroprostheses - Visual, Neural Interfaces - Neural stimulation, Neural Interfaces - Neuroimaging
Abstract:

Keywords: Motor Neuroprostheses - Epidural Stimulation, Neural Interfaces - Computational modeling and simulation, Human Performance - Modelling and prediction
Abstract:

Keywords: Brain Stimulation-Deep brain stimulation, Neurological disorders, Neural Interfaces - Computational modeling and simulation
Abstract:

Keywords: Sensory Neuroprostheses - Auditory, Sensory Neuroprostheses, Neural Interfaces - Sensors and body Interfaces
Abstract:

Keywords: Neural Interfaces - Neuroimaging, Neural Interfaces - Recording, Brain-computer/machine Interface
Abstract:

Keywords: Neural Interfaces - Neural stimulation, Brain Stimulation - Sensory restoration, Sensory Neuroprostheses - Visual
Abstract:

Keywords: Neural Interfaces - Neural stimulation, Neural Interfaces - Computational modeling and simulation, Sensory Neuroprostheses - Visual
Abstract:

Keywords: Neural Interfaces - Computational modeling and simulation, Sensory Neuroprostheses - Visual, Neural Interfaces - Neural stimulation
Abstract:

Keywords: Neural Interfaces - Implantable systems, Brain-computer/machine Interface, Neural signal processing
Abstract: In this paper, we propose a reconfigurable neural spike classifier based on neuromorphic event-based networks that can be directly interfaced to neural signal conditioning and quantization circuits. The classifier is set as a heterogeneity based, multi-layer computational network to offer wide flexibility in the implementation of plastic and metaplastic interactions, and to increase efficacy in neural signal processing. Built-in temporal control mechanisms allow the implementation of homeostatic regulation in the resulting network. The results obtained in a 90 nm CMOS technology show that an efficient neural spike data classification can be obtained with a low power (9.4 uW/core) and compact (0.54 mm2 per core) structure.

Keywords: Neural Interfaces - Biomaterials, Neural Interfaces - Regeneration and tissue-electrode Interface
Abstract: With the advent of genetically-encoded optical tools to trigger or report neuronal activity, new designs for multielectrode arrays (MEAs) used in neural interfacing incorporate both optical and electrical modes of stimulating or recording neural activity. Likewise, the need to improve upon the biocompatibility of implanted MEAs has moved the field towards the use of softer, more compliant materials in device fabrication. However, there is limited available information on the impact of the materials used in MEAs on the function of interfaced individual neurons and neuronal networks. We assessed the responses of rat cortical neurons on optically transparent materials commonly used in the construction of “next-generation” devices: indium tin oxide (ITO), parylene-C, and polydimethylsiloxane (PDMS). We found that neuronal network formation and spiking responses to electrical stimulation were enhanced in neurons cultured on ITO. We observed reduced excitability and synaptic connectivity between neurons cultured on PDMS. We hypothesize that the superior conductivity of ITO and suboptimal neuronal attachment to PDMS contributed to our results.

Keywords: Neural Interfaces - Regeneration and tissue-electrode Interface, Neural Interfaces - Neural microsystems and Interface engineering, Neural Interfaces - Implantable systems
Abstract: Silicon neural probes with sophisticated integrated CMOS-based electronics providing a large number of recording sites integrated along the slender probe shanks have become a driving force in neural engineering throughout the past decade. Using such neural probes in a chronic setting often requires to mechanically anchor them with respect to the skull. Any relative motion between brain and implant causes however tissue responses such as glial scarring, thereby the recordable neurons are shielded from the electrodes integrated on the probe, and the signal quality is decreased. In the current work, we present preliminary results obtained using a mechanically fixed and a floating silicon neural probe implanted into the cortex of a non-human primate. We demonstrate that the neural signal quality of the floating probe is superior to the fixed approach. Nonetheless, also the skull-fixed probe allowed to identify action potentials and stably record low frequency signals over the entire recording period.

Keywords: Neural Interfaces - Recording, Neural Interfaces - Neural microsystems and Interface engineering, Neural Interfaces - Microelectrode and fabrication technologies
Abstract: The NTera2/D1 (NT2) cell line (hNT) is an alternative cell line source for human neurons. In this paper, we demonstrate for the first time how it is possible to record high quality spontaneous spikes from human hNT neurons on gold electrodes in an SU-8 trench. This is comparable to the only other previous hNT neurons recording performed on titanium nitride (TiN) electrodes with commercial systems.

Keywords: Neural Interfaces - Neural microsystems and Interface engineering, Neural Interfaces - Microelectrode and fabrication technologies, Neural Interfaces - Neural stimulation
Abstract: Miniaturization of electrodes is a prerequisite of selective and targeted interaction with single neurons, enabling more applications in the continuously growing field of neuroprostheses. Miniaturization in all three dimensions of the electrical contact sites should maintain or increase longevity and electrical functionality. The thin-film metallization of the electrode site, which is only a couple of hundreds of nanometers thick, has to withstand high chemical load through the corrosive environment in the body and the electrochemical processes during electrical stimulation in vivo. Platinum (Pt), which is known to be chemically inert and mechanical stable as bulk material shows a lack of chemical and mechanical integrity applied in thin-film microelectrodes. In our study we investigated failure mechanisms of thin-film Pt electrodes under conditions of electrode aging and electrical stimulation in different physiological media. To understand and eventually overcome stability loss, we investigated the intrinsic structural stress and deformations that arose from mechanical loading through chemical impact and electrical stimulation using optical microscopy and white-light interferometry. Electrochemical measurements indicated oxidation and surface roughening as two of the degradation processes in thin-film electrodes. From the results presumptions about the underlying microstructural changes were made.

Keywords: Neural Interfaces - Microelectrode and fabrication technologies, Neural Interfaces - Biomaterials, Neural Interfaces - Implantable systems
Abstract: A significant and recognized problem in implantable neural recording and stimulation probes is operational lifetime. It is well known that both electrophysiological recording and, to a lesser extent, stimulation probes suffer severe performance degradation over periods ranging from months to a few years. Performance degradation of implantable probes arises due to a number of factors, including systemic host response, glial scarring, cellular responses to non-inert materials and, importantly, probe material degradation and delamination. SiC has superior chemical inertness and molecule barrier properties that result in insulation endurance for long term applications in bodily fluids. We present a microfabrication process that employs both insulating and conducting silicon carbide thin films to produce a novel type of implantable neural probes which present no heterogenous material interfaces to the biological environment. The exterior of our devices is composed of seamless transitions between doped and insulating SiC, completely encapsulating metal traces necessary to reduce track resistivity. The fabrication process is specifically designed such that the only interfaces presented to the extracellular fluid are made entirely from SiC, thus rendering these arrays very resistant to long-term delamination and interface failure. This process presents an important path towards neural implants for truly chronic applications; the fabrication method can be used for other neural probes.

Keywords: Neural Interfaces - Computational modeling and simulation, Brain-computer/machine Interface, Motor neuroprostheses
Abstract: Optical techniques such as two-photon (2p) calcium imaging have the potential to transform the way we interrogate neural circuits, both in the realm of basic neuroscience and in the development of brain-machine interfaces (BMIs). This may be possible by overcoming some of the limitations of electrophysiological methods. Here we ask if optical imaging signals, in particular 2p calcium imaging signals from GCaMP6, can benefit BMIs despite their relatively long activity-response time constants, low signal-to-noise ratios (SNRs), and slow acquisition frame rates. We employed motor cortical electrode array recordings as the basis for generating synthetic 2p GCaMP signals. We then decoded movement kinematics from these surrogate data using a state-of-the-art BMI decoder algorithm. We found that it was possible to decode the position and velocity of the hand from synthetic imaging signals. We quantified the decoder performance using standard mean squared errors (MSEs) and Pearson’s correlation coefficient (r) measures. Decode quality varied considerably as a function of SNR and the frame rate of data acquisition. Future computational and experimental research is required to quantify SNR more accurately and to increase the imaging frame rate while maintaining high SNR, in order to improve all-optical BMI (o-BMI) performance. This study should help establish the feasibility and design space of o-BMIs.

Keywords: Neural Interfaces - Microelectrode and fabrication technologies, Neural Interfaces - Recording, Neural Interfaces - Neural microsystems and Interface engineering
Abstract: Microfabrication technology can enable extracellular neural recording electrodes with unprecedented wiring density, and the ability to benefit from continued CMOS technology scaling. Key building blocks for a neural probe include recording sites that sense electrical activity inside the brain, and the subsequent routing of signals to neural amplifiers outside the brain. We here introduce a scalable circuit model for recording sites and signal routing, valid for different amplifier integration approaches. We define noise and cross-talk requirements, and analyze how future CMOS technology scaling will drive the ability to record from increasingly large number of sites in the mammalian brain. This analysis provides an important step in understanding how advances of MEMS and CMOS fabrication can be utilized in large-scale recording efforts of many thousands to possibly millions of neurons.

Keywords: Neural Interfaces - Recording, Neural Interfaces - Neural stimulation, Neural Interfaces - Implantable systems
Abstract: In this work we propose an energy-efficient, implantable, real-time, blind Adaptive Stimulation Artifact Rejection (ASAR) engine. This enables concurrent neural stimulation and recording for state-of-the-art closed-loop neuromodulation systems. Two engines, implemented in 40nm CMOS, achieve convergence of <42μs for Spike ASAR and <167μs for LFP ASAR, and can attenuate artifacts up to 100mVp-p by 49.2dB, without any prior knowledge of the stimulation pulse. The LFP and Spike ASAR designs occupy an area of 0.197mm2 and 0.209mm2, and consume 1.73μW and 3.02μW, respectively at 0.644V.

Keywords: Neural Interfaces - Microelectrode and fabrication technologies, Neural Interfaces - Neural microsystems and Interface engineering
Abstract: Electrical impedance spectroscopy is used to study the retina and the vitreous humor which can help in designing retinal stimulation experiments. Electrical stimulation of retina is done to artificially stimulate retinal neurons for restoring vision in blind patients. The stimulus is given through microelectrodes which are placed over the retina. Since retina is an inhomogeneous conductive medium with ionic species as charge carriers, electrical characterization of the retina as well as the tissue interface is required. Vitreous humor comes in between tissue and electrode which has properties different from that of retina. Change in vitreous properties can alter the experimental results for stimulation of the retina. Thus calculation of electrical properties of both vitreous and retinal tissue are necessary for designing of stimulus parameters. Goat retina is used in this study for characterization which can help in further using this animal in stimulation experiments. It has been found that the RGC side of isolated retina has lower resistance to current flow compared to the photoreceptor side. It has also been found that changes in the vitreous properties that might affect the tissue stimulation parameters can be measured through impedance spectroscopy.

Keywords: Neural Interfaces - Computational modeling and simulation, Sensory Neuroprostheses, Neural signal processing
Abstract: In this paper, the interaction between axons in nerve trunks has been characterized in the statistical sense while the mechanism model is investigated based on two different observation scales. In order to describe the interaction phenomena, a symmetric coupling factor matrix has been presented which is considered as a transverse mode combining the standard cable equation. It has been shown that the presented model is an extension of the existing individual membrane potential models. The simulation results indicate that the responses of the nerve fibres are sensitive to the couplings and the action potential could be generated by other coupled axons even if there is no external artificial stimulation.

Keywords: Neural Interfaces - Computational modeling and simulation, Neural signal processing, Neuromuscular Systems - Computational modeling and simulation
Abstract: Identification of intended movement type and movement phase of hand grasp shaping are critical features for the control of volitional neuroprosthetics. We demonstrate that neural dynamics during visually-guided imagined grasp shaping can encode intended movement. We apply Procrustes analysis and LASSO regression to achieve 72% accuracy (chance = 25%) in distinguishing between visually-guided imagined grasp trajectories. Further, we can predict the stage of grasp shaping in the form of elapsed time from start of trial (R2=0.4). Our approach contributes to more accurate single-trial decoding of higher-level movement goals and the phase of grasping movements in individuals not trained with brain-computer interfaces. We also find that the overall time-varying trajectory structure of imagined movements tend to be consistent within individuals, and that transient trajectory deviations within trials return to the task-dependent trajectory mean. These overall findings may contribute to the further understanding of the cortical dynamics of human motor imagery.

Keywords: Neural Interfaces - Microelectrode and fabrication technologies, Neural Interfaces - Regeneration and tissue-electrode Interface, Neural Interfaces - Neural microsystems and Interface engineering
Abstract: In this study, we describe a novel peripheral-nerve interface which makes use of highly flexible multi-electrode arrays that are integrated into hydrogel-based scaffolds to form a hybrid tissue-engineered electronic construct. This tissue-engineered electronic nerve interface (TEENI) is designed to be scalable to high channel counts using multiple polyimide-based “threads” that are evenly distributed through a volume of the nerve equal to its diameter times the distance between one or more nodes of Ranvier. Such scalability could greatly increase the precision and resolution of motor-control and sensory-feedback signals exchanged between amputees and advanced upper-limb prosthetic devices.

Keywords: Neural Interfaces - Neural stimulation, Neurorehabilitation, Neuromuscular Systems - Peripheral mechanisms
Abstract: This paper reports on the design and optical characterization of a single laser to multiple optical fiber (SLMOF) device for optogenetics research. Output end of SLMOF is an optical fiber bundle made by 210 optical fibers arranged into a 7×30 matrix. Single laser beam produced by laser diode (LD) is delicately steered on optical table to be coupled into target optical fiber. By controlling the power of the LD and quickly switching among fibers, this device allows for high flexibility when designing spatiotemporal light patterns. We tested the optical characterization of the device both in vitro and in vivo. Light was emitted from the end of the fiber at a power of 275.4 mW/mm2 when a drive current of 80 mA was used. The transmission fraction was 18.6% after penetrating 0.2 mm of spinal cord slice, and 1.3% after 1 mm. Coupling with 50 ms of repetitive stimulation at 10 Hz produced an only 0.348 K temperature increase at the spinal cord surface. The capacity of the system for optogenetics research was also demonstrated by epidural spinal cord stimulation induced hindlimb motion in paralyzed rats expressing channelrhodopsin-2 (ChR2).

Keywords: Neural Interfaces - Regeneration and tissue-electrode Interface, Neural Interfaces - Neural microsystems and Interface engineering, Neural Interfaces - Biomaterials
Abstract: Investigating intracerebral tissue at single cell level has become increasingly important in animal studies and meanwhile found its way into clinical applications. The stability of single unit activity (SUA) to be recorded can be however quite elusive, which -among others- might be caused by the mechanical mismatch of stiff neural implants and soft brain tissue. Unfortunately, the variety of factors influencing signal degradation over time are not perfectly understood yet. One opportunity to determine degradation onsets and possibly identify relevant mechanisms influencing signal stability is impedance spectroscopy of the electrode-brain interface. In particular in view of high-channel count electrode arrays used in neuroscientific research, an impedance spectroscopy analyzer is needed, capable of non-destructively and continuously measuring impedance spectra of a large number of electrodes. The measurement system developed in the current study allows determining the impedance spectra from up to 1024 channels with low uncertainty and an accuracy within a 15%-window over a wide range of impedance values.

Keywords: Neural Interfaces - Neural stimulation, Neurorehabilitation
Abstract: Infrared neural stimulation (INS) provides a contact free, high spatial resolution method to regulate neural activities. Due to photothermal effect, neurons which located in the beam path, could be depolarized by infrared light. How to keep the high efficiency of opto-thermal transduction in nerve tissue, and meanwhile increase the photon penetration depth in targeted tissues is the key question in current INS. Here in our study, we tried to use 980nm laser light to increase photon penetration depth in tissues, and use carbon nano-particles as photon absorber to enhance the photothermal effect of INS in sciatic nerve. The compound muscle action potentials are evoked and the radiant threshold reduces by about 2 times with carbon nano-particles. In vitro temperature measurements detect a significant temperature increment in target neurons with carbon particles during laser irradiation. These findings demonstrate that carbon nano-particles can improve the efficiency and enhance infrared neural stimulation.

Keywords: Neural Interfaces - Neural stimulation, Neural Interfaces - Computational modeling and simulation
Abstract: Transcutaneous Electrical Nerve Stimulation (TENS) attracts extensive attention in neuromodulation due to its minimal invasiveness. Conventional TENS uses non-invasive surface planar electrodes attached to the skin for delivering the stimulation current to modulate neural circuitries. Previous studies have shown that using these surface planar electrodes in TENS therapy may cause stimulation-induced skin irritation. In order to validate the cause and solve this problem, a bioheat transfer study in a realistic human spinal cord model was built based on finite element method (FEM) and a spiked electrode is proposed to avoid skin burning by taking into consideration both the electrical and thermal properties of the layered skin. Results show the peak temperature of using the spiked electrode for stimulation is around 37.1 ˚C, much lower than using the planar electrode (51.6 ˚C). Moreover, in contrast to the traditional surface electrodes, the peak necrosis fraction of using the spiked electrode is 0.02, indicating that the whole biological model is thermally safe and does not cause skin irritation.

Keywords: Neural Interfaces - Neuroimaging, Neural Interfaces - Recording, Brain-computer/machine Interface
Abstract: In rodent’s olfactory bulb, different odors evoke distinct glomeruli patterns to form a specific ‘Odor Map’. The optical imaging has the ability to visualize the functional responses in precise spatial and temporal resolution, which provides an opportunity to decipher the functional architecture of the patterns in the olfactory bulb. Here we adapted intrinsic signal imaging to visualize the odor response in the olfactory bulb. The imaging signals displayed clear odor-evoked pattern with stability over repeated stimulation. The imaging results also revealed a more focal response in comparison with electrophysiological activity. These characteristics suggest the possibility to use the optical imaging signals as the input of a brain-machine interface.

Keywords: Neural Interfaces - Microelectrode and fabrication technologies, Neural Interfaces - Recording, Neural Interfaces - Implantable systems
Abstract: A 16-channel implantable microelectrode array (MEA) was designed and fabricated by Micro-electromechanical Systems (MEMS) technology. Then the electrode sites were modified with platinum/graphene oxide nanocomposites (Pt-GO) by one-step electrochemical deposition and later handled by cyclic voltammetry reduction in phosphate buffer solution. Finally, a Nafion thin film was coated on the MEA surface. The prepared MEA sensor was intended for simultaneous detection of dopamine (DA) and electrophysiological signals. DA response performance was tested by chronoamperometry at potential of 150mV, results showed the microelectrode had a sensitivity of 17.5 pA/µM to dopamine and detection limit as low as 10nM (S/N=3). The linear range was 1 × 108 mol / L to 3.5 × 104 mol / L. Besides, it had very low response to interferences such as Ascorbic Acid, Uric Acid and 5-hydroxytryptamine. After modification, the impendences were dropped from 627.28 kΩ to 35.46 kΩon average and background noise was significantly reduced for electrical signal recording in vitro compared with bare microelectrodes. These results show the modification of Pt-rGO-Nafion on electrode surface can be a suitable way for dopamine and electrical signal detection and promising to apply in dual-mode signals measurement in vivo.

Keywords: Neural Interfaces - Microelectrode and fabrication technologies, Neural Interfaces - Neural stimulation, Neural Interfaces - Biomaterials
Abstract: Abstract— In our central nervous system (CNS), glutamate is an excitatory neurotransmitter that can be accumulated remarkably by brain tissue. As is noticeable from previous studies, glutamate is somehow closely related with most ways of normal and abnormal cerebral activities. It was significant to monitor glutamate concentration changes in vivo real time. Therefore, we developed an enzyme-based microelectrode array with high sensitivity (7.34pA/μM) and perfect linearity (R2=0.999). Selectivity to normal neurotransmitters satisfied our in vivo demands. Ultimately, we applied the electrode into the brain specific tissue to study glutamate excitatory pathway in hippocampus CA3 by offside KCl and glutamate stimulating. The hippocampus CA3 glutamate was detected about 18.17μM and 13.19μM by twice KCl stimulating in the offside cortex, and 6.50μM, 4.55μM by twice glutamate stimulating. All peak current back to the base level later. The self-regulation ability reflects the glutamate excitatory pathway from hippocampus CA3 to cortex.

Keywords: Neural Interfaces - Neural microsystems and Interface engineering, Neural signal processing
Abstract: In this work, we present a simple technique for building interconnected 3-Dimesional (3D) neuronal populations by means of PDMS (poli-dimethyl-siloxane) structures coupled to Micro-Electrode Array (MEA) substrates. Particularly, we designed a confinement structure for obtaining two structurally and functionally interconnected 3D in vitro neuronal networks. The inter-population links are obtained by means of two groups of ten micro-channels with specific spacing and heights. This experimental model presents a real 3D architecture mimicking the in vivo condition and exhibiting spontaneous activity with structural and functional connections between the two compartments. We characterized the dynamics of these 3D interconnected assemblies in terms of spiking and bursting activity and we estimated the connectivity by means of a recently developed cross-correlation based algorithm. The obtained results suggest new avenues for the use of such an in vitro 3D model for studying brain (dys)function and for co-culturing neuronal cells towards the development of ‘organ-on-chip’ microsystems.

Keywords: Neural Interfaces - Neural stimulation, Neurorehabilitation - Wearable systems, Neural Interfaces - Computational modeling and simulation
Abstract: Migraine is a socioeconomic burden, whose pharmaceutical and invasive treatment methods may have troublesome side-effects. A wearable neuromodulator targeting frontal nerve branches of trigeminal nerve may provide an effective solution to suppress or treat migraine. Such solutions have had limited efficacies. In this paper, using computational models, the relationship of this lack of efficacy to some neural variations is investigated. The results indicate that due to neuro-anatomic variations, different current levels may be required to achieve a sufficient level of neural stimulation. Thus, an optimized design should consider such variations across the patient group.

Keywords: Neural Interfaces - Biomaterials, Neural Interfaces - Implantable systems, Neural Interfaces - Neural microsystems and Interface engineering
Abstract: Although the potential for intracortical implanted microelectrodes has been demonstrated, successful clinical translation has been hindered by their inability to function over clinically relevant time-points (years to decades). Failure of implanted microelectrode arrays (MEA’s) has been highly correlated with the foreign body response which progressively encapsulates the MEA’s in a glial sheath, isolating them from the surrounding microenvironment. To mitigate this response, drug delivery has been implemented to release therapeutics from the device surface. This has allowed limited success at acute time points; however, challenges in maintaining long-term therapeutic dosages has resulted in an inability to mitigate chronic inflammation. A recent publication has demonstrated the use of multi-layer film composed of dextran-sulfate, minocycline hydrochloride (MH), and gelatin type A, assembled via layer-by-layer technology, capable of providing sustained release of MH for several weeks; however, their impact on functionality has not yet been analyzed. We found that after being coated with 20 layers NeuroNexus devices exhibited significantly increased impedance at 100Hz, 1kHz, and 10kHz, though this was significantly reduced after 24-hours of incubation in PBS. Charge carrying capacity also significantly increased after incubation in PBS. It can be concluded that these coatings do influence MEA’s immediately after coating, but is less impactful over time as the coating degrades.

Keywords: Neural Interfaces - Microelectrode and fabrication technologies, Neural Interfaces - Implantable systems, Neural Interfaces - Neural microsystems and Interface engineering
Abstract: Maintaining the insulation between adjacent electrical interconnections is critical for the success of active implantable medical device. Underfilling and globtop coating of dense arrays of microfabricated interconnects poses a big reliability risk. Contamination, voids and the difficulty to deposit liquid underfillers with the appropriate adhesive behavior remains a major hurdle when developing miniaturized high-channel neural interfaces. We approach a bottom up process to fabricate adhesion promoting, graded interfaces on the bottom and top side of polyimide-based microelectrode arrays. This allows the use of pre-molded silicone rubber gaskets as dry underfill material and silicone rubber as globtop material. In this work we present the layer deposition approach to solve the difficulties of providing a double-sided, inversely oriented layer stack for an adhering silicon-oxide termination on polyimide substrates. By introducing a sacrificial polyimide layer we permit high temperature depositions of the required layers allowing a release of the fabricated stack at the desired interface. Long term stable silicone rubber underfill and overcoat is thus achievable despite the use of polyimide substrates. The fabricated samples showed better adhesion to silicone rubber even after storing in phosphate buffered saline (PBS) at 85 °C for 18 hours and at 60°C for 72 hours. The FTIR spectrum also revealed the integrity of structural stack after detachment from release layer.

Keywords: Neural Interfaces - Sensors and body Interfaces, Brain-computer/machine Interface, Neural signal processing
Abstract: Drowsiness is one of the most prevalent causes of accidents in mining, driving and industrial activities carrying high personal risks and economic costs. For this reason, automatic detection of drowsiness is becoming an important application, and it is being integrated in a large variety of wearable and deeply embedded systems. Relevant effort has been spent in the past to quantify the drowsiness level from behavioral features exploiting eye tracking systems, dermal sensors or steering wheel movements. On the other hand, all these approaches lack of generality, they are highly intrusive and can only be applied in specific circumstances. A promising alternative approach is based on the extraction and processing of physiological features from the EEG using Brain Computer Interfaces (BCI). This work describes a wearable system capable of detecting drowsiness conditions and emitting alarms using only EEG signals, with three levels of alarm based on the blink duration and on the spectral power of alpha waves. This implementation aims to replace or complement the use of cameras and other sensors, extracting drowsiness information exploiting both behavioral and physiological features from EEG sensors only. The system was validated with 7 test subjects achieving detection accuracy of 85%, while being much more lightweight and compact than other state of the art methods.

Keywords: Neural Interfaces - Neural stimulation, Brain Stimulation - Sensory restoration, Sensory Neuroprostheses - Visual
Abstract: Electrical microstimulation and near-infrared (NIR) laser light irradiation were combined to control neural activities in cerebral cortex in vitro. Spatio-temporal patterns of suprathreshold neural excitations in the primary visual cortex of mouse cerebral slices were visualized by the Ca²⁺-sensitive dye imaging. In response to a single pulse of the biphasic stimulus current delivered to the layer IV, neural excitations were initiated around the stimulating electrode tip, and then synaptically propagated to the layer II/III. The neural excitations and population excitatory post-synaptic potential in the layer II/III were inhibited in a spot region where NIR laser light was illuminated. This inhibitory effect was larger when the carbon-nanotube-bundle fiber as a light absorber was placed within the spot region. The present results suggested that the NIR laser light can be used to locally inhibit trans-synaptic neural excitations induced by electrical microstimulations.

Keywords: Neural Interfaces - Neural stimulation, Brain Stimulation-Deep brain stimulation, Neurological disorders - Epilepsy
Abstract: The requirements of exploring multi-site responsive stimulation for epilepsy treatment have driven the development of neurostimulator with more recording channels, more stimulation channels, and more powerful on-board calculation. In this paper, a multi-channel closed-loop stimulation system based on FPGA platform, which implements 32-channel 30kS/s recording and 4-channel arbitrary waveform (up to 1mA, ±15V voltage compliance) constant-current stimulation, was adopted for seizure control on kainic-acid injected rodents. Seizure can be detected autonomously within the FPGA and trigger flexible stimulation patterns using a custom application on PC. Four triple-electrodes were implanted into rat’s motor cortex, CA1, CA3 and STN for signal recording and electrical stimulation. The responsive stimulations were delivered once seizure patterns were detected on any channel. In-vivo results showed various epileptiform electrographic patterns at different site with different onset time. The multi-site stimulation in different brain area rather than single target stimulation are promisingly a better strategy for seizure control.

Keywords: Neural Interfaces - Neuromorphic engineering
Abstract: Non-invasive brain machine interfaces (BMIs) on motor imagery movements have been widely studied and used for many years to take advantage of the intuitive link between imagined motor tasks and natural actions. En route to future technical applications of neuromorphic computing, a major current challenge lies in the identification and implementation of brain inspired algorithms to decode recorded signals. Neuromorphic computing is believed to allow real-time implementation of large scale spiking models for processing and computation in non-invasive BMIs. Taking inspiration from the olfactory system of insects, we advance and implement a novel approach to decode and predict imaginary movements from electroencephalogram (EEG) signals. We use a spiking neural network implemented on SpiNNaker (4-chip, 64cores) neuromorphic hardware. Our work provides a proof of concept for a successful implementation of a functional spiking neural network for decoding two motor imagery (MI) movements on the SpiNNaker system. The approach can be extended to classify more complex MI movements on larger SpiNNaker systems.

Keywords: Neural Interfaces - Microelectrode and fabrication technologies, Neural Interfaces - Neural microsystems and Interface engineering, Neural Interfaces - Recording
Abstract: Neural networks in the central nervous system are composed of neurons which interconnect through their axons. Although computational capacity of axons is of great interest, their small structure makes exact evaluation difficult. Culture device capable of obtaining features of individual axons and stimulating with pharmacological reagent is needed. In this study, we aimed to develop a novel culture device for evaluating and stimulating axons. First, the culture device was fabricated and neuronal cells were cultured. Their axons were maintained more than 30 days and axonal conductions were detected with substrate-embedded electrodes. Second, developmental changes in conduction delay and activity-dependent changes in conduction velocity were evaluated. As a result, it is suggested that axons of younger neurons have lower conduction velocity and easily change their conduction velocity by their hiring frequency. These results show that our culture device is feasible to record conduction through individual axons and evaluate their feature.

Keywords: Brain Functional Imaging - EEG and Evoked Potentials
Abstract: Abstract—In China, students are mainly divided into two groups, i.e., the arts and science students, according to their superiority. In the present study, we intended to probe their superiority based on the constructed brain networks by combining various reference technologies with network analysis. And our present findings showed that only the reference electrode standardization technique (REST) revealed the reasonable results that for the science students, their daily study facilitated the brain network to be reasonably constructed, i.e., higher network efficiency, for the corresponding information processing. And after-task resting-state the arts students could compensate their weaknesses by the useful daily training.

Keywords: Neural Interfaces - Implantable systems, Neural Interfaces - Regeneration and tissue-electrode Interface
Abstract: Regenerative peripheral nerve interfaces are a novel method for integrating with the peripheral nervous system. These devices have the potential to isolate and transduce both afferent (sensory) and efferent (motor) neural signals to produce fine control of advanced prosthetics. We have developed a novel regenerative device comprised of microfabricated polyimide electrode threads supported by a hydrogel scaffold containing methacrylated hyaluronic acid, collagen I, and laminin to enable intimate contact with regenerating axons. While this advanced device holds theoretical promise for establishing a stable chronic neural interface, it also requires a novel surgical approach in comparison to current existing methods of peripheral neural interface technologies. Here we describe the development of the surgical methodology required for successful chronic implantation of the TEENI device in the rat sciatic nerve.

Keywords: Neural Interfaces - Regeneration and tissue-electrode Interface
Abstract: Neural-interface devices have the potential to isolate and transduce both afferent (sensory) and efferent (motor) neural signals of the peripheral nerve to and from electrical signals in instrumentation for stimulation and recording to produce fine control of advanced prosthetics. In order to potentiate the full spectrum of possible applications, the persistent foreign-body response needs to be addressed. Here we describe the cellular and extracellular components of chronically implanted polyimide threads suspended within a tricomponent hydrogel. The results of these experiments will contribute to design modifications for future fabrications of tissue-engineered-electronic-nerve-interface (TEENI) devices.

Keywords: Neural Interfaces - Neural stimulation, Neural Interfaces - Neuroimaging, Neural signal processing
Abstract:

Keywords: Neural Interfaces - Sensors and body Interfaces, Neuromuscular Systems - Wearable systems
Abstract: This paper describes a wearable smart Bluetooth mouse based on EOG (electrooculography) for people with physical disability. The eye movement can be detected by the electrodes, which are located on the side of the eye. Through the eye movement, the cursor on a smartphone can be moved to anywhere of the screen by a designed pattern recognition algorithms.

Keywords: Neural Interfaces - Neural stimulation, Neural Interfaces - Computational modeling and simulation, Sensory Neuroprostheses - Visual
Abstract: Retinal neuroprostheses or ‘bionic eyes’, aim to restore patterned vision to those with vision loss by electrically stimulating the remaining neurons in the degenerate retina. Despite considerable progress over the last two decades, such devices generally stimulate indiscriminately both the ‘ON’ and ‘OFF’ pathways in the retina, conveying highly non-physiological signals to the brain. In this study, we used computational models to explore the ability of a new approach to retinal neurostimulation. To elicit neural responses more closely resembling those of natural vision, we demonstrate preferential excitation of the ON and OFF neurons by delivering spatiotemporally patterned stimuli with a multi-electrode array. In particular, the strategy relies on: (1) strategic placement of electrodes at key anatomical position such as RGC body and optic disc, and (2) using modulated stimulus waveforms, tailored to each stimulus electrode, to either initiate or suppress neural responses travelling to the brain. By providing preferential activation of ON and OFF cells, and hence better device-to-biology integration, retinal implants would convey more physiologically-realistic spiking patterns to the brain, potentially improving the efficacy of next generation retinal implants.

Keywords: Neural Interfaces - Microelectrode and fabrication technologies, Neural Interfaces - Biomaterials, Neural Interfaces - Implantable systems
Abstract: Long-term stability of neural interfaces is a challenge that has still to be overcome. In this study, we manufactured a highly stable multi-layer thin-film class of carbon-based devices for electrocorticography (ECoG) incorporating silicon carbide (SiC) and amorphous carbon (DLC) as adhesion promoters between glassy carbon (GC) electrodes and polyimide (PI) substrate and between PI and platinum (Pt) traces. We aged the thin-film electrodes in 30 mM H2O2 at 39 °C for one week - to mimic the effects of post-surgery inflammatory reaction - and subsequently stressed them with 2500 CV cycles. We additionally performed stability tests stimulating the electrodes with 15 million biphasic pulses. Finally, we implanted the electrodes for 6 weeks into rat models and optically characterized the explanted devices. Results show that the fabricated ECoG devices were able to withstand the in vitro and in vivo tests without significant change in impedance and morphology.

Keywords: Neural Interfaces - Neuroimaging, Neural Interfaces - Recording, Neurological disorders - Stroke
Abstract: The feasibility of utilizing concurrent electroencephalography (EEG) and Near infrared spectroscopy (fNIRS) recording to characterize the difference in cortical activities between stroke patients (n=3) and healthy controls (n=3) was evaluated with a motor execution task. The asymmetry indicators, including inter-hemispheric sample entropy (IHI_En) derived from EEG signal and inter-hemispheric Oxygenated hemoglobin (IHI_HbO) concentration change derived from the fNIRS signal were extracted and compared. The IHI_En and IHI_HbO showed an observable difference between the stroke patient and healthy control groups. The results demonstrated the feasibility of utilizing the concurrent EEG-fNIRS approach to assess the recovery progress of post-stroke patients with motor impairment.

Keywords: Neural Interfaces - Computational modeling and simulation, Brain Stimulation-Deep brain stimulation, Brain physiology and modeling - Neuron modeling and simulation
Abstract: Mitigating pathological synchrony of neurons in basal ganglia networks was considered as one of the potential mechanisms of deep brain stimulation (DBS) in treating Parkinson’s disease. Motivated by reducing the energy of external stimuli, optimal control strategies are presented to regulate DBS waveform so as to mitigate synchronous oscillations of neural networks with fewer energy expenditure. In this paper, the adaptive optimal control of DBS based on reinforcement learning (RL) is designed to desynchronizing phase models of neural populations in the presence of noise. Numerical simulations show the effectiveness of the proposed method. Moreover, the influence of noise intensity on the control performance of the controller is analyzed.

Keywords: Neural Interfaces - Neural stimulation, Neural Interfaces - Sensors and body Interfaces, Neuromuscular Systems - Wearable systems
Abstract: In this paper, we present the design and performance of a portable, arbitrary waveform, multichannel constant current electrotactile stimulator that costs less than 30 in components. The stimulator consists of a stimulation controller and power supply that are less than half the size of a credit card and can produce +/-15 mA at +/-150 V. The design is easily extensible to multiple independent channels that can receive an arbitrary waveform input from a digital-to-analog converter, drawing only 0.9 W/channel (lasting 4-5 hours upon continuous stimulation using a 9 V battery). Finally, we compare the performance of our stimulator to similar stimulators both commercially available and developed in research.

Keywords: Neural Interfaces - Computational modeling and simulation, Neural Interfaces - Neural stimulation, Sensory Neuroprostheses - Visual
Abstract: Visual prosthesis based on penetrative optic nerve (ON) stimulation is an alternative approach to partially restore visual function. As one of the most important factors, pulse shape of electrical stimulus is a major consideration in the design of ON visual prosthesis. Animal experiments showed that the evoked responses in visual cortex (V1) to ON electrical stimulation are dependent on the stimulus. The purpose of this research is to study the response properties of ON fibers under electrical stimulation in different shapes by mathematical modeling, in order to explore and explain the response mechanisms of ON to varied external electrical stimuli. Based on a mixed model of penetrating electrode and multiple ON fibers, we investigated the performance of a variety of stimulus waveforms in recruiting ON fiber responses by calculating recruitment area (RA). The simulation results provided a preliminary explanation for the V1 response properties to various ON electrical stimuli in the previous animal study. Besides, theoretical basis is formed for further design and choice of stimulus waveform in ON prosthesis.

Keywords: Neural Interfaces - Computational modeling and simulation, Sensory Neuroprostheses - Visual, Neural Interfaces - Neural stimulation
Abstract: Epiretinal prostheses have been reported to restore partial visual function of patients suffering from retinal degenerative diseases. The design of stimulating electrode array of the epiretinal prosthesis is one of the pivotal factors to influence its performance and spatial resolution. Because the cell density distribution of retinal ganglion cells(RGCs) and retinal anatomic structure varied with the eccentricity, stimulating electrodes should be designed specifically at different retinal eccentricities to obtain optimal visual restoration performance. In this study, computational models of epiretinal electrical stimulations in varied retinal eccentricity with variant RGCs densities in the temporal of center macular and peripheral macular areas were constructed. The activation thresholds and amount of excited RGCs were investigated and compared in these two areas under electrical stimulation with different pulse parameters, electrode sizes and electrode-retina distances. These modeling approaches can be used to further study the activated characters of RGCs in response to extrinsic electrical stimulations in different retinal areas, and improve the performance of epiretinal prostheses by optimizing stimulation strategies and electrode design.

Keywords: Neural Interfaces - Recording, Neural signal processing, Brain-computer/machine Interface
Abstract: The corticospinal tract (CST) is one of the descending tracts that carry the forelimb volitional information. Using flexible multi-electrode arrays (MEAs), the CST signals were recorded in rats during active and resting states. The power spectral density (PSD) of CST signals during the active state were notably higher than those observed during resting or anesthesia. Average inter-channel coherences below 30 Hz were considerably higher for reach-to-pull task compared to face grooming and the quiet state, suggesting the presence of volitional information in the recorded CST signals. A novel feature extraction technique (spectrum of cross-spectrum) was applied to obtain feature vectors and used in a classification algorithm. The extension and flexion phases of the forelimb movements were classified with 95% accuracy. The results warrant further investigations for extraction of more detailed volitional motor control information from the CST signals.

Keywords: Neural Interfaces - Neuroimaging, Brain Functional Imaging - fMRI, Brain Functional Imaging - Connectivity and Network
Abstract: Physiology noise is a problem that suffering spinal cord resting state fMRI. This study applied ICA method to minimize the influence of physiology noise of spinal cord resting state fMRI. The results showed that applying ICA to denoise physiology noise is very effective.

Keywords: Neural Interfaces - Implantable systems, Neural Interfaces - Neural stimulation
Abstract: In this paper, a high-density 128-channel retinal prosthesis ASIC is proposed. Chip architecture is introduced and one-channel simulation results are presented. Using X-FAB 0.35μm high-voltage process, the chip has an area of 6mm×4.8mm and could generate maximum 500μA bi-phasic stimulating current.

Keywords: Neural Interfaces - Biomaterials, Brain-computer/machine Interface, Neurological disorders
Abstract: Low impedance electrodes are critical for the development of high signal-to-noise ratio (SNR) neural recording devices. In this study, template biocompatible polymer microspheres were coated with conducting polymer and analyzed with scanning electron microscopy (SEM), impedance spectroscopy (IS) and cyclic voltammetry (CV).

Keywords: Neural Interfaces - Sensors and body Interfaces, Neural Interfaces - Neural stimulation, Neurological disorders - Psychiatric disorders
Abstract: This study investigated the increase of brain-derived neurotrophic factor (BDNF) expression by western blot analysis, when irradiated with photonic light on the HT-22 cell line. From the experiments, we found that the photonic light source irradiation on HT-22 cells increased BDNF expression. Thus, photonic light irradiation of 660 nm is effective for recovery of memory mechanism of hippocampal cell.

Keywords: Neural Interfaces - Computational modeling and simulation, Brain-computer/machine Interface, Neural Signal Processing - Time frequency analysis
Abstract: Our experiment was designed to classify the attended visual feature in a stimulus picture in SSVEP signals while one observing a visual stimulus with dual features, for example, color and orientation. We use stacked auto-encoder deep learning neural network with two hidden layers to build and evaluate the classification model. The result shows that the classification performance of single-feature stimuli is significantly better than that of dual-feature stimuli.

Keywords: Neural Interfaces - Recording, Neural Interfaces - Neural stimulation, Neural Interfaces - Neuromorphic engineering
Abstract: Neural stimulators and implantable systems represent one of the most promising technologies to reduce neurological impairments. Here we present a promising prosthesis prototype capable to restore the communication between damaged in vitro neuronal circuitries.

Keywords: Neural Interfaces - Implantable systems, Neural Interfaces - Neural stimulation, Neurological disorders
Abstract: Long term variability in therapeutic impedance following deep brain stimulation has not been extensively investigated across different brain targets. We retrospectively examined impedance data from 866 DBS patients. Aside from slight impedance drifts in the subthalamic nucleus, we found that impedance tends to stabilize in all targets over time.

Keywords: Neural Interfaces - Implantable systems, Neural Interfaces - Neural stimulation, Neurological disorders - Diagnostic and evaluation techniques
Abstract: Essential Tremor (ET) is defined as a rhythmical, involuntary oscillatory movement of the limbs and is one of the most common movement disorders. Intention tremor occurs mostly in the upper limbs (with slow oscillations between ~4-12 Hz) during the initiation and execution of goal-directed reaching motions, while it is absent at rest. Although the pathophysiological basis of ET remains unknown, a pathological synchronous oscillation in a neuronal network involving the thalamus, especially the ventral intermediate nucleus (Vim), the premotor (PM) and primary motor (M1) cortices, and the cerebellum has been suggested [1]. It is assumed that deep brain stimulation (DBS) suppresses tremor by masking the “tremor cells” in the Vim. This study aims to control the stimulation delivery by leveraging the presence of specific biomarkers such as movement intention (mu rhythm on motor and premotor cortices), presence of tremor (rhythms on Vim, and accelerometer), coherence between electrocorticography (ECoG) and accelerometer. The rationale is to decrease DBS-related side effects, such as speech and balance impairments, decrease battery depletion, while delivering an equally effective treatment.

Keywords: Neural Interfaces - Implantable systems, Neural Interfaces - Neural stimulation, Neural Interfaces - Regeneration and tissue-electrode Interface
Abstract: An implantable neural stimulation in freely moving animals has important application in evaluating the safety and efficacy of various neuromodulators including deep brain stimulation, peripheral nerve stimulation and spinal cord stimulation. The aim of this study is to develop an implantable wireless neural stimulation system for animal experiments. The system is composed of an external power transmitter and a miniature implantable wireless stimulator. This developed system doesn’t have batteries and external lead wires. The experimental animal is placed in a customized cage surrounded by three orthogonal coils which are used to generate a magnetic field. An electric current is induced in implanted coils then regulated to produce biphasic current pulses. This developed system can be used for chronic electrical stimulation in pathophysiological and behavioral experiments with freely moving animals.

Keywords: Neural Interfaces - Neural stimulation, Neural Signal Processing - Time frequency analysis
Abstract: Mandarin speech is very complex because the fundamental frequency changes over time, and the mechanism underlying tone discrimination remain still unclear. Here, we recorded single unit response to one word with four tones in the inferior colliculus (IC) of anesthetized guinea pigs. We tested how well the activities of single neuron could be used to discriminate among four tones and we compared discriminability to the characteristic frequency (CF). The results showed that single IC neuron could be used to discriminate among different tones with a range of ability, ranging from chance to 89%. The ability to discriminate among different tones of one word was correlated the CF of single neuron. The preliminary results suggested IC neurons could discriminate Mandarin tones.

Keywords: Neural Interfaces - Recording, Neuromuscular Systems - EMG models, processing and applications, Neurorehabilitation - Robotics
Abstract: Improving the function of artificial arms remains a considerable challenge. A steady and high-efficiency wireless system can help patients endure longer training sessions without the obstacles associated with traditional wired training systems. We have developed a portable professional wireless multimodal information recording system that provides more freedom and high flexibility to patients undergoing rehabilitation training. The preliminary results demonstrate the reliability and feasibility of a portable-sized wireless sEMG acquisition system in prosthetic limb training.

Keywords: Neural Interfaces - Sensors and body Interfaces, Brain Functional Imaging - EEG and Evoked Potentials, Neural Interfaces - Recording
Abstract: This paper presents the design and implementation of an 8-channel and low-noise chopper-stabilized analog front-end (AFE) for EEG acquisition system. Each channel of the AFE is composed of an AC-coupled chopper instrumentation amplifier (ACCIA), a programmable gain amplifier (PGA) and buffer. The proposed AFE is implemented in 0.35 μm CMOS technology with the ADC, MUX, digital part and other control blocks. Post-layout simulation results exhibit that the AFE achieves 46/52/58/64 dB programmable gain, 88 dB CMRR, 107 dB PSRR, and 0.33 μVrms input-referred noise for a bandwidth of 0.5-150 Hz. Each channel is consuming 7.9 μA/CH from a 3 V supply.

Keywords: Neural Interfaces - Computational modeling and simulation
Abstract: For execution of complicate movements, coordinated plan of muscle movement is necessary. This implies that involvement of high order brain function is expected in initiation stages of movement rather than in later stages. Several studies of the graph theory have shown that the higher order brain area forms efficient network characterized by the high clustering coefficient and lower mean path length. However, no study has compared network properties of motor cortical areas between initial and later movement stages. In this study, we have investigated neuronal connectivity varying with acceleration or deceleration of reaching movements. Analyses based on the graph theory revealed that the node degree increased and the shortest path length decreased when hand movement accelerated, and vice versa. Our results imply that a neuronal network of the primary motor area forms a more efficient network for the initiation of movements.

Keywords: Neural Interfaces - Regeneration and tissue-electrode Interface
Abstract: Abstract— We present a microfluidic neuron culture platform capable of isolating and guiding CNS axons from neuronal somata where physical damage to axons can be easily performed for studying axon regeneration under localized biomolecular treatments.

Keywords: Neural Interfaces - Microelectrode and fabrication technologies, Neural Interfaces - Neural microsystems and Interface engineering, Neural Interfaces - Implantable systems
Abstract: The assembly of active implantable medical devices requires robust and reliable fabrication procedures and methods. In best case, it is free of individual fabrication skills. However, on research level and within fabrication of customized implants, the application of standardized procedures is difficult to realize. A laser-structured and metalized small ceramic plate was designed to reliably interconnect a customized, helically wound 16-channel cable with a commercially available circular Omnetics connector. The small plate enables a more reliable soldering of the single wires to the connector. The application is not restricted to the assembly of cables, but can be easily changed due to the rapid prototyping capability of lasers.

Keywords: Neural Interfaces - Biomaterials, Neural Interfaces - Microelectrode and fabrication technologies, Neural Interfaces - Regeneration and tissue-electrode Interface
Abstract: A variety of patterning methods have been developed to engineer culture substrates, however, most of existing methods cannot control network distribution during cultivation and maturation. Here, we developed a dynamic patterning method utilizing gold nanorods and agarose hydrogel and applied this technique on microelectrode arrays. Using this platform, agarose hydrogel patterned on microelectrode arrays was melted to connect two neuronal networks. After melting, neurites grew through the melted area and the correlation of signals between two networks increased over time. The developed method is expected to be useful for manipulating in vitro neuronal networks for neuroscientific researches.

Keywords: Neural Interfaces - Microelectrode and fabrication technologies, Neural Interfaces - Regeneration and tissue-electrode Interface, Neural Interfaces - Implantable systems
Abstract: This article deals about the characterization and follow up of implanted parylene/platinum microelectrodes. After a 12 weeks implantation on a rat model, we can report that these microelectrodes are suitable for in-vivo use. Moreover, the follow up of the electrochemical impedance combined with electrical stimulation highlighted the reduction of the impedance after electrical stimulation. This work was included in the INTENSE project, one of the goal of which was to prove the feasibility of selective neural recording or stimulation with cuff electrodes

Keywords: Neural Interfaces - Biomaterials, Neural Interfaces - Microelectrode and fabrication technologies, Neural Interfaces - Implantable systems
Abstract: We present here a comparison between boron doped diamond and rough platinum iridium based on electrochemical impedance spectroscopy (EIS) and histological analysis. After a 12 weeks implantation on a mini-swine model of multielectrode arrays made from each material, we can report that the coating of Boron Doped Diamond (BDD) did not impair the biocompatibility of the electrode array. Moreover the histology showed a glial scar for both multielectrode array but confirmed the long term biocompatibility of boron doped diamond.

Keywords: Neural Interfaces - Recording, Motor neuroprostheses, Neural Interfaces - Neural stimulation
Abstract: Intrafascicular peripheral nerve interfaces offer the ability to record and stimulate from individual or small groups of axons. Such an intimate neural interface can be used to provide sensation or record motor intent for an amputee. However, such devices tend to degrade in performance over time. To address this issue, flexible carbon nanotube (CNT) yarns were evaluated as chronic, bidirectional intrafascicular interfaces. Two rats were implanted in different fascicles of the sciatic nerve, namely the tibial and peroneal fascicles; under freely moving animal conditions, neural recordings (ENG) were taken simultaneous with EMG of the gastrocnemius and tibialis anterior muscles. Correlation coefficients of 0.8±0.23 between the recorded ENG and EMG over 6 weeks were recorded.

Keywords: Neural Interfaces - Neural microsystems and Interface engineering, Neural Interfaces - Implantable systems, Neural Interfaces - Recording
Abstract: Previously, we presented a fabrication method of a flexible penetrating microelectrode array, with characterization results of its mechanical and electrical properties as a neural signal recording tool. In this study, we aimed to investigate the feasibility of the flexible and penetrating microelectrode array for long-term intra-fascicular nerve signal recording.

Keywords: Neural Interfaces - Neuromorphic engineering, Brain Stimulation - Transcanial magnetic stimulation (TMS), Neural signal processing
Abstract: This paper introduce a novel human-to-human interaction based on direct human brain-to-brain interface (BBI) and muscle-to-muscle interface (MMI) in a closed-loop control paradigm. Electroencephalography (EEG) and electromyography (EMG) are adopted as noninvasive recording methods, while functional electrical stimulation (FES) and transcranial magnetic stimulation (TMS) are employed as stimulation methods in order to accomplish the closed loop data flow through natural and artificial pathway.

Keywords: Neural Interfaces - Sensors and body Interfaces, Sensory Neuroprostheses - Somatosensory and vestibular, Sensory Neuroprostheses
Abstract: In this paper, we describe the design of a compliant sensorized finger for a prosthetic hand. We designed a flexible printed circuit board that houses three pressure sensors and can easily wrap inside our compliant finger. Our flexible printed circuit board will enable the finger to measure pressure while still being robust to impact from multiple directions.

Keywords: Neural Interfaces - Sensors and body Interfaces, Motor Neuroprostheses - Prostheses, Neurorehabilitation - Wearable systems
Abstract: Previous research in targeted muscle reinnervation(TMR) has been promising in prostheses control. To further improve the function of prostheses, we applied pattern recognition(PR) in conjunction with conventional amplitude based control to classify the surface electromyogram(EMG) signals recorded over the reinnervated muscles.