Keywords: Neuromuscular Systems - Wearable systems, Human Performance - Modelling and prediction
Abstract: Epilepsy is a long-term neurogenic disease that requires caregivers to accompany the patient days and nights. Caregivers have to help the patients immediately when they are having a seizure, which could cause vital injuries or even death. To address this issue, we designed a bracelet containing a three-dimensional accelerometer and a three-dimensional gyroscope to record the movements of the patient and built a Random Forest model to automatically detect seizures in at most 10 seconds upfront. We designed a home-based data-collecting method that allows patients to stay at home or perform their daily activities outside the hospital. Data collected in this method would be similar to the situation in which the patients would actually use wearable monitoring devices at home. The performance was evaluated based on an experimental study of epilepsy detection and classification, where epileptic motor data was collected in the West China Hospital of Sichuan University. Due to the experimental results, our daytime seizure detection model achieved 75.91% sensitivity and 88.90% precision, while our nighttime seizure detection model achieved 88.01% sensitivity and 88.33% precision. These preliminary results indicate that this home-based data collection method can capture seizures efficiently.

Keywords: Brain-computer/machine Interface, Neurorehabilitation - Neurofeedback, Neuromuscular Systems - Neurorehabilitation
Abstract: Brain-computer interfaces (BCIs) have been widely used to improve or restore neural functions. For stroke patients, BCIs based on motor training show a promising potential in motor rehabilitation. However, the neural mechanism and the effects of different time course in motor rehabilitation remain unclear. To this end, our study focused on the BCI based neurofeedback training (NFT) design and its evaluation method. During motor imagery and execution (MI/ME) tasks, electroencephalogram (EEG) and electromyogram (EMG) were synchronously recorded and probed. We found the multi-band changes of coupling EEG-EMG features. Additionally, the long-term motor NFT significantly improved the cortical-muscle activation, while non-feedback training improved less. These relevant results give a theoretical basis to the development and application of new neural rehabilitation technology.

Keywords: Brain Functional Imaging - NIR, Neurorehabilitation - Robotics, Human Performance - Gait
Abstract: Human locomotor control ranges on a spectrum of automaticity, from highly automatic strategies that require minimal cognitive input, to attention-demanding executive-control strategies. The neural circuitry that facilitates automaticity is impaired by stroke, resulting in a compensatory shift toward executive-control, as well as reduced paretic propulsion and increased step-to-step variability. We have developed a soft robotic exosuit to augment paretic propulsion by providing paretic plantarflexor assistance during the propulsive phase of walking. For this preliminary study, we hypothesized that changes in walking automaticity would accompany changes in paretic propulsion. When plantarflexor assistance timings were tuned to reduce propulsion variability—a biomechanical measure of automaticity—a -14.7±2.5% variability reduction was accompanied by increased paretic propulsion (%Δ:+6.4±6.3%) and prefrontal cortex activity (Δ oxygenated hemoglobin:+1.08E-04±1.05E-04 M mm). When plantarflexor assistance timings were instead tuned to reduce prefrontal cortex activity—a neurophysiological measure of automaticity—a -1.3E-05±1.1E-05 M mm decrease in oxygenated hemoglobin was accompanied by both increased paretic propulsion (%Δ:+4.4±8.1%) and reduced propulsion variability (%Δ:-3.7±19.3%). Biomechanical and neurophysiological measures of automaticity are sensitive to exosuit assistance timing changes, but are differentially affected, highlighting the need for indiv

Keywords: Neurological disorders, Clinical neurophysiology, Human performance
Abstract: The unobtrusive monitoring of vital signals and behaviour can be used to gather intelligence to support the care of people living with dementia. This can provide insights into the persons wellbeing and the neurogenerative process, as well as enable them to continue to live safely at home, thereby improving their quality of life. Within this context, this study investigated the deployability of non-contact respiration rate (RR) measurement based on an Ultra-Wideband (UWB) radar System-on-Chip (SoC). An algorithm was developed to simultaneously and continuously extract the respiration signal, together with the confidence level of the respiration signal and the target position, without needing any prior calibration. The radar-measured RR results were compared to the RR results obtained from a ground truth measure based on the breathing sound, and the error rates were within 8% with a mean value of 2.5%. The target localisation results match to the radar-to-chest distances with a mean error rate of 5.8%. The tested measurement range was up to 5m. The results suggest that the algorithm could perform sufficiently well in non-contact stationary respiration rate detection.

Keywords: Brain-Computer/Machine Interface - Robotics applications, Neurorehabilitation - Robotics, Neuromuscular Systems - Wearable systems
Abstract: Assistive and Wearable Robotics have the potential to support humans with different types of motor impairments to become independent and fulfil their activities of daily living successfully. The success of these robot systems, however, relies on the ability to meaningfully decode human action intentions and carry them out appropriately. Neural interfaces have been explored for use in such system with several successes, however, they tend to be invasive and require training periods in the order of months. We present a robotic system for human augmentation, capable of actuating the user's arm and fingers for them, effectively restoring the capability of reaching, grasping and manipulating objects; controlled solely through the user's eye movements. We combine wearable eye tracking, the visual context of the environment and the structural grammar of human actions to create a cognitive-level assistive robotic setup that enables the users in fulfilling activities of daily living, while conserving interpretability, and the agency of the user. The interface is worn, calibrated and ready to use within 5 minutes. Users learn to control and make successful use of the system with an additional 5 minutes of interaction. The system is tested with 5 healthy participants, showing an average success rate of 96.6% on first attempt across 6 tasks.

Keywords: Neurological disorders - Stroke, Brain Functional Imaging - fMRI, Neurorehabilitation
Abstract: Motor imagery training (MIT) is an adjunctive approach for motor recovery after stroke. It has been suggested as a "backdoor" to motor system at all stages of stroke recovery, especially for patients with severe motor impairment. However, the current MIT-related brain reorganization evidences were obtained mainly from mild stroke patients, little is known about severe patients. In this study, we recruited 12 first-ever stroke patients with moderate to severe upper limb impairment and investigated their brain reorganization after MIT using task-state fMRI. During passive movement of the unaffected hand, the activation exhibited a normal contralateral-lateralized pattern. However, during passive movement of the affected hand, ipsilateral compensational activation was observed in the contralesional sensorimotor areas, and the compensational activation in the contralesional precentral gyrus was significantly decreased after MIT. Our study expanded the current understanding of neural mechanism of MIT to stroke patients with moderate to severe deficits.

Keywords: Brain physiology and modeling, Neural signal processing, Brain functional imaging
Abstract: The overarching goal of The Virtual Brain Cloud (TVB-Cloud) is personalized prevention and treatment of dementia. TVB-Cloud builds a generic framework for managing the life cycle of sensitive data to leverage the potential of big personal data and high-performance computing. To achieve generalizable results that help individual patients, the TVB-Cloud integrates data of large cohorts of patients and healthy controls through multiscale brain simulation with The Virtual Brain (TVB) simulator. Infrastructures for sharing and processing health data at a large scale in compliance with the EU General Data Protection Regulations (GDPR) are presently missing. The TVB-Cloud consortium closes this gap and thus makes health data actionable. The VirtualBrainCloud is funded by the European Commission Horizon 2020 program. The VirtualBrainCloud combines the expertise of 17 interdisciplinary partners in the EU: 12 research institutions - including a supercomputing center, four SMEs and one patient organization. The VirtualBrainCloud collaborates with other SMEs and platform projects. Indoc Research, a Canadian not-for-profit organization, has been contracted for technical coordination of the project The Virtual Brain Project EU Flagship Human Brain Project with its e-brain-research-infrastructure EBRAINS The European Open Science Cloud Architecture (EOSC) TVB-Cloud plans to be accessible through the EOSC marketplace once in production.

Keywords: Neurological disorders - Diagnostic and evaluation techniques
Abstract: Alzheimer´s Disease is the most common neurodegenerative disorder. The neuropathological process of AD could start even 20 years before the typical age for the initial cognitive impairment symptoms. The disease is characterized by the pathological accumulation of amyloid protein deposits as well as the phosphorylation of the Tau protein. These two neuropathological events induce a series of physiological changes including anatomo-functional network disruption that can be captured with Electrophysiological techniques. The alterations of the oscillatory activity (power or phase synchrony) constitute early features in the continuum of the disease.

Keywords: Neurological disorders - Diagnostic and evaluation techniques, Neural Signal Processing - Time frequency analysis, Brain physiology and modeling - Neural dynamics and computation
Abstract: Continuous neuronal degeneration in Alzheimer’s disease (AD), altering local and inter-areal brain networks, makes it difficult for accurate prognosis. Evidence from animal models suggest that AD originates from changes in to excitation/inhibition (E/I) balance in neuronal networks. However, how such changes in E/I balance in AD would be reflected in neuronal dynamics in humans and whether these could be used for AD diagnosis and prognosis is not known. Here, we used magnetoencephalography (MEG) data and estimated long-range temporal correlation (LRTC) along with functionally estimated E/I. We show that subjective cognitive decline (SCD) and mild cognitive impairment (MCI) subjects differ in theta, alpha and beta band from healthy normal control (NC) group. These differences are in-line with the notion of E/I imbalance and could be noticed as early as in SCD people.

Keywords: Neural Signal Processing - Time frequency analysis, Brain Functional Imaging - EEG and Evoked Potentials
Abstract: Rapid eye movement sleep Behavior Disorder (RBD) is a parasomnia that involves violent and undesirable behaviours during the Rapid Eye Movement (REM) sleep [1]. RBD patients have a high-risk of developing Parkinson Disease (PD) or Dementia with Lewy Bodies (DLB) and cognitive impairment is among the major risk factors for phenoconvertion. Increased phase-synchronization is thought to reflect the activation of a compensatory mechanism that counter balance the cognitive decline in Mild Cognitive Impairment [2]. In this work, we aimed to investigate whether altered phase synchronization and amplitude correlation could be linked to the balancing of cognitive impairment in a longitudinal cohort (N=18, 17 men, mean age 69.7 ± 7.5 years) of idiopathic RBD (iRBD) patients. We found that phase synchronization increases in the alpha band and amplitude correlation decreases in the delta band at the follow-up. These changes are prominent between central and posterior regions, and are correlated with overall cognitive impairment.

Keywords: Brain Functional Imaging - MEG, Brain Physiology and Modeling - Neural circuits
Abstract: A reduced model of the default mode network is presented, which nodes are composed by realistic layered-microcircuits. Importantly, the model takes in account the excitatory/inhibitory balance deterioration characterizing the early stages of Alzheimer’s disease (AD). The model is able to reproduce some of the signatures involved in the AD continuum, as the increased occurrence of transient discharges and increased connectivity in key regions of the AD continuum.

Keywords: Motor learning, neural control, and neuromuscular systems, Brain-computer/machine Interface, Human Performance - Sensory-motor
Abstract: In this first presentation for the suggested mini-symposium “Movement augmentation with supernumerary limbs” we (i) introduce and discuss different types of movement augmentation and (ii) present findings from a study with human subjects with six-fingered hands that show augmented manipulation abilities [1] and from a study on concurrent control of an ECoG based brain-computer interface (BCI) and natural movements [2].

ACKNOWLEDGEMENTS We thank M Akselrod, L Bashford, M Mace, H Choi, M Blüher, A-S Buschhoff, T Pistohl, R Salmon, A Cheah, O Blanke, A Serino, E Burdet, J Wu, D Sarma, K Collins, RPN Rao, JG Ojeman, J Eden, D Farina, M Bräcklein for their contributions to the work presented here.

REFERENCES [1] Mehring, C., Akselrod, M., Bashford, L. et al. Augmented manipulation ability in humans with six-fingered hands. Nat Commun 10, 2401 (2019). https://doi.org/10.1038/s41467-019-10306-w [2] Bashford L et al. Concurrent control of a brain-computer interface and natural overt movements, J. Neural Eng., 15(4) 066021

Keywords: Neural Interfaces - Sensors and body Interfaces, Human performance, Brain-Computer/Machine Interface - Robotics applications
Abstract: Body interfaces that move with one limb. e.g. a foot interface, can be used to control a robotic arm. However, the use of such interfaces for controlling a supernumerary robotic limb (SL) in coordination with the natural limbs (NL) has not been well studied. In our presentation, we will first review recent studies on trimanual control with body interfaces using one foot and the arms. We will then present a recent study that was conducted with 14 subjects to evaluate human performance of trimanipulation with haptic feedback. The results of trimanipulation with the hands and foot of a single subject exhibited similar or even superior performance than similar operation carried out by two cooperating subjects.

Keywords: Neural Interfaces - Recording, Neural signal processing, Motor learning, neural control, and neuromuscular systems
Abstract: Supernumerary artificial limbs controlled concurrently with natural limbs would allow extending human motor capacities beyond anatomical boundaries. However, to ensure flexible and parallel navigation of supernumerary and natural limbs, control signals independent from those activating natural degrees-of-freedom are needed. We introduce a novel approach of extracting true augmented control signals by using a non-invasive neural interface to facilitate spectral separation of the neural code of spinal motor neurons. We demonstrate that spectral components outside the bandwidth of musculoskeletal control, i.e. inside the beta range (13-30Hz), can be partially uncoupled from low-frequency components and thus from voluntary muscle contractions.

Keywords: Neurorehabilitation - Robotics, Neurorehabilitation - Wearable systems, Human performance
Abstract: Supernumerary robotic limbs (SRL) are wearable robotic devices that promise to augment the wearers' motor capabilities and coordinate with their natural limbs. However, current SRLs are controlled with little robust and hardly flexible automatic behaviours inspired from conventional robotics. We introduce here a concept of intuitive voluntary control of an SRL, exploiting an approach we recently developed for prosthetics limbs relying on the minimization of the body compensation of the user. We present preliminary results obtained on the control of a virtual SRL to perform advanced manipulation task involving both hands and the SRL. These preliminary results illustrate the possibility offered by this control strategy and by controllable SRL in general to enhance operator’s manipulation abilities.

Keywords: Human Performance - Sensory-motor, Human performance, Neuromuscular Systems - Wearable systems
Abstract: We present a system to provide the user with real time proprioceptive feedback regarding the state of a supernumerary robotic limb (SRL). The system converted the robot kinematics into a vibration amplitude-frequency value, using a custom electronic board. Four eccentric-motors placed on the leg delivered the vibrotactile pattern to subjects. We measured the accuracy in real-time tracking of the robot end-effector position and the delay from the robot movement onset. We tested four subjects in a preliminary study, and we found an average Position Error and a Delay of 0.084 ± 0.01 m and 1.169 ± 0.408 s respectively, which validated the feasibility of the presented setup. Increasing the learning phase duration should further improve subject performance. Additionally, the present platform could easily be employed to test the efficacy of dynamic feedbacks (such as joint angles and torques) for real time tracking of SRL.

Keywords: Brain-Computer/Machine Interface - Biofeedback, Brain Functional Imaging - EEG and Evoked Potentials, Human Performance - Cognition
Abstract: This paper aims to provide an overview on new bioengineering perspectives in terms of mental and emotional measures in humans (e.g. workload and stress), by using biosignals recording and processing, for out-of-the-lab applications. In this regard, recording technologies and brain-computer interface methodologies experienced a significant growth in the recent years, allowing to reach the right maturity to enter the market in the human factors and ergonomics fields.

Keywords: Neuromuscular Systems - Wearable systems, Human Performance - Fatigue, Human Performance - Attention
Abstract: Driving safety is significantly dependent on human factors, most particularly the psychological state of the driver. Traffic errors, accidents and injuries have been linked to driver fatigue, driver stress and poor vigilance. In the advent of automated driving, these human factors will continue to pose serious safety concerns, due to human mental disengagement and reduced capacity to intervene during emergency automation failures. These major concerns of public safety are motivating strong interest in cognitive monitoring systems that can track these mental states. This driver information can then be relayed to the vehicle control system, which could then trigger alerts or activate driver-assistance systems. Frontal alpha EEG was found to be a neural correlate of trust-in-automation, with potential for future trust monitoring using streamlined wearable technology. Next, we will present practical solutions using EEG data only from the non-hair-bearing brain (NHB) areas. Using the Theta Beta Ratio of NHB we could predict the driver interactions with automated vehicle with conditional automation interactions. The ultimate aim is to develop wearable solutions that are accurate in measuring these mental states, convenient to wear, and comfortable for prolonged use without disrupting driver performance.

Keywords: Brain-computer/machine Interface, Neural signal processing, Human Performance - Ergonomics and human factors
Abstract: Human-machine trust has largely been monitored subjectively, based on self-reported measures, with studies attempting only recently to seek objective measures for trust by surpassing difficulties in capturing the complex aspects of this multifaceted cognitive state. Nevertheless, recent progress in neurophysiological sensors development, as well as neuroimaging technology and cognitive neuroscience have brought the perspective for objective trust monitoring to reality. A particular area of application in which research on objective trust measurement has been thriving is that of intelligent vehicles and, particularly, the interaction between human drivers and autonomous vehicles. In this presentation we outline several aspects for understanding the cognitive, affective and behavioural components of driver trust, and identifying neural correlates of human-autonomous vehicle trust using behavioural, physiological and brain-based measurements. Future directions for improving trust monitoring towards practical implementation are also discussed.

Keywords: Brain-computer/machine Interface, Brain Functional Imaging - Multimodal, Human Performance - Attention
Abstract: Neuroergonomics provides interesting prospects to measure the brain out the lab. We present recent research dedicated to implement brain computer interface in actual flight condition. We show that different mental states such as mental workload, mental fatigue, mental overload, inattentional deafness to auditory alarm and working memory can be detected with high accuracy with portable brain imaging techniques.

Keywords: Human performance, Human Performance - Ergonomics and human factors, Human Performance - Attention
Abstract: Advanced automated driving vehicles (AVs) are expected to radically transform road transport. To facilitate deployment research has vastly focused on ensuring the safety of AVs operation, investigating primarily the interaction between the human driver (or user depending on the level of automation) and the AV. In addition to the interaction between the AV and its users, the successful deployment of AVs depends also on the exchange between AVs and other road users. For instance, how shall AVs communicate their intentions to their users and/or other road users, or what do cyclists and pedestrians anticipate when interacting with AVs? Focusing on vehicles with advanced automated driving features, that is vehicles that allow the human driver to not execute the driving task when automation is engaged , this paper contributes to the overall discourse on development and deployment of automated driving by reviewing the literature on trust and human-machine interaction research. On the one hand, the state-of-the-art is identified and technological advances discussed. On the other hand, current limitations and associated concerns are explored and proposals on how future research should be designed presented. We expect our findings to provide new insights on the interaction between AVs and road users, and be instrumental for relevant policy makers.

Keywords: Neural Interfaces - Microelectrode and fabrication technologies, Neural Interfaces - Neural microsystems and Interface engineering, Neural Interfaces - Biomaterials
Abstract: The advent of organic bioelectronics has introduced a deep change within the field of in vitro neural interfaces. In fact, the development of novel organic devices and materials deeply broadened the plethora of possible solutions for the development of innovative electrophysiological tools. At the same time, neural in vitro interfaces are evolving toward the study of ever more refined brain organoids, which represent a very important, and long sought-out, change of paradigm in the field. We here present a flexible, multifunctional, and reference-less organic transistor-based device with tridimensional recording sites for in vitro electrophysiology, which can represent an interesting and convenient solution for brain-on-a-dish applications.

Keywords: Neural Interfaces - Microelectrode and fabrication technologies, Neural Interfaces - Recording, Neural Interfaces - Neural microsystems and Interface engineering
Abstract: The use of graphene transistors for transducing neural activity extends the spatiotemporal resolution of electrophysiological methods to lower frequencies and enables the implementation of multiplexing strategies.

Keywords: Brain Functional Imaging - EEG and Evoked Potentials, Brain Functional Imaging - Connectivity and Network, Neural signal processing
Abstract: Olfactory perception recruits multiple neurocognitive processes, that are implemented across distributed but highly interactive brain areas housed primarily in the frontal and temporal lobes. Given that these brain areas have different functions and activity characteristics, the mechanisms supporting functional interactions between them are unclear. To address this knowledge gap, we analyzed EEG cross-frequency coupling (CFC) between frontal to left vs. right temporal regions during exposure to fragrances of varying subjective pleasantness. Our results show that higher-pleasantness fragrances gave rise to left-lateralized α - γ coupling and right-lateralized θ - γ coupling, which likely represent emotional processing and memory processing respectively. Regression models reveal that fragrance pleasantness exhibits a nonlinear relationship with αγ laterality, but a linear relationship with θ-γ laterality. These findings illustrate the significant role that CFCs play in long-distance neural communication in olfactory perception. Their robust dynamics in response to subjective odor evaluation render them as promising neural features for olfactory brain-computer interfacing.

Keywords: Brain Functional Imaging - EEG and Evoked Potentials, Brain-computer/machine Interface
Abstract: The high inter-subject variability in emotional EEG activities has posed great challenges for practical EEG-based affective computing applications. The recently popular domain adaptation strategy seemed to be a promising technique for addressing this issue, by minimizing the discrepancy of EEG data from different subjects. The present study proposed and implemented an extended Domain Adaptation method by introducing Subject Clustering (DASC). By clustering subjects based on the similarity of their emotion-specific EEG activities, the DASC method could make a flexible use of the available source domain information towards an optimized target domain application. Using the publicly available EEG dataset of DEAP, the DASC method achieved an average accuracy of 73.9±13.5% and 68.8±11.2% for binary classifications of the high or low levels of valence and arousal. Comparison with the state-of-the-art performance as well as the ablation experiments suggest the proposed DASC method as an effective extension to the conventional domain adaptation methods for EEG-based emotion recognition.

Keywords: Brain Functional Imaging - EEG and Evoked Potentials, Human Performance - Cognition, Human Performance - Attention
Abstract: Assessing cognitive load may be useful in a variety of applications, especially in identifying the onset of high cognitive load. However, current methods do not exist that can pinpoint such an event. We recorded EEG from participants while they completed auditory oddball paradigm and Stroop tasks. To determine the time at which a change in cognitive load could be detected, we applied auditory tones at four time points before and after the onset of an incongruent Stroop trial: (1) 100 ms prior to the Stroop onset, (2) 100 ms post Stroop onset, (3) 300 ms post Stroop onset, and (4) 450 ms post Stroop onset. Event-related potential results suggest that cognitive load was highest at time points after 100 ms post Stroop onset. This work provides a method for identifying an increase in cognitive load, which could be an important diagnostic tool for device development and tuning.

Keywords: Brain Functional Imaging - EEG and Evoked Potentials, Neural signal processing, Sensory Neuroprostheses - Signal and vision processing
Abstract: Steady-state visually evoked potentials (SSVEPs) occur due to a repetitive visual stimulus, which results in periodic responses from the visual cortex at the stimulus frequency and its harmonics. Prior studies show that the fundamental SSVEP frequency response can be used to produce a visual reconstruction of what is shown to the human eye. However, due to interference coming from the source and the sensing device, the resulting captured image contains salt-and-pepper noise and random value noise. This study investigates whether information present in the SSVEP harmonics is useful in denoising and enhancing the captured visual reconstruction. The proposed convolutional neural network architecture methods are compared against the SSVEP fundamental and naive additive reconstructions. The results show that combining harmonics and reconstructions from different signal processing methods into the neural network architecture enhances the resulting image.

Keywords: Brain Functional Imaging - EEG and Evoked Potentials, Neural Signal Processing - Time frequency analysis, Neural signal processing
Abstract: Modern audiometry is largely a behavioural task, with the pure-tone audiogram (PTA) being the gold standard for evaluating frequency-specific hearing thresholds in adults. The nature of behavioural audiometry makes estimating accurate hearing thresholds difficult in infants and people with disabilities, where following instructions or interacting with the test may be difficult or impossible. We propose a method in which Auditory Evoked Potentials (AEPs) are used as an alternative to behavioural audiometry for detecting frequency-specific thresholds. Specifically, P300 responses elicited by the tones of a PTA are automatically detected from electroencephalogram (EEG) data, to evaluate hearing acuity. To assess the effectiveness of this method, we created a dataset of EEG recordings from participants presented with a series of pure tones at 6 different frequencies with steadily decreasing volumes, during a PTA test. This dataset was used to train a support vector machine (SVM) to identify when a participant was played a tone, whether they perceived it or not using their EEG. Results demonstrate that detecting hearing events can be very accurate for participants for whom the classifier has been trained a-priori. However, accuracy drops significantly for unseen participants -- when the classifier has not been trained on any prior data from a given participant before classifying their EEG.

Keywords: Brain Functional Imaging - EEG and Evoked Potentials, Neuromuscular Systems - EMG models, processing and applications, Neural signal processing
Abstract: Nowadays, a study of the functional relationship between the activations of large scale brain and muscular networks while a subject is using an upper-limb exoskeleton is still missing. Here, we investigated directed functional connectivity (DFC) between cortical topographical maps (EEG-maps) and muscle synergies (M-Syn). We found causal links within EEG-maps during motor execution and not at rest. A rich pattern of frequency-dependent unidirectional influence between EEG-maps and M-Syn was also found. These results suggest that these low-dimensional descriptors still retain physiological information and indicate DFC patterns between them as potential new biomarkers of motor impairment.

Keywords: Human performance, Neuromuscular Systems - EMG models, processing and applications, Neuromuscular Systems - Learning and adaption
Abstract: A common challenge in myoelectric control is to create reliable human-machine interfaces. We have developed a robust surface electromyography (EMG) command system that relies on training the human rather than training a classifier, thereby enabling the human to correct for signal changes. The purpose of the current study was to understand whether adding an adaptive timing feature and classifiers to our EMG interface could improve the performance of trained human subjects. Forty-eight subjects participated in the experiment, where they learned four different commands to control a cursor to select fixed targets on a computer screen, where each command was composed of a combination of short and long muscle contractions. A Command Accuracy Test assessed subject proficiency at producing commands when prompted. The command classification accuracy was calculated for a control condition and two conditions that reflected possible adaptive features: the timing between EMG signal inputs and a subject-specific classifier. The overall results showed significant improvements in command classification accuracy for both adaptive components (p < 0.0001) compared to the control. However, some initially high performing subjects did not receive as much benefit. These results suggest that customizing the sEMG command system for individual subjects could improve their performance.

Keywords: Human Performance - Attention, Brain-Computer/Machine Interface - Robotics applications, Neurorehabilitation - Robotics
Abstract: Recently, Human-Robot Interaction (HRI) researchers have paid lots of attentions on the conversational robots that can provide didactic or pedagogy teachings, especially for the children and young adolescents with cognitive disabilities, such as autism and epilepsy. The research and developments of such robots for educational purposes are investigated intensively. Above all the difficult challenges, how to evaluate the effectiveness of a conversational robot, which mimics a teacher communicating with a student, to improve the performance of learning and studying, is the key factor to deploy such robots in our society and be widely adopted. However, we haven’t seen much investigation in previous literatures so far. In order to bridge the gap, this preliminary study had explored the use of conversational robot with electroencephalogram (EEG) biosignals as evidence measurements to improve the self-learning performance during COVID-19 pandemic crisis, while the schools are forced to close, and the students are inevitably segregated in social isolations. We had collected 10 student participants’ EEG data, which were calculated to find concentration levels, and then the robot had conversations with the students adaptively according to his/her concentration levels. The result showed that the conversations between robot and humans, who are constantly not concentrating on the learning tasks, could effectively increase his/her level of concentrations.

Keywords: Human Performance - Cognition, Human performance
Abstract: Songs can express emotions and contain both lyrics and music. Early behavioral studies have shown that songs are influenced by the presence of lyrics for emotional processing. This work further examined the event-related potential (ERP) N400 responses of participants to elucidate whether lyrical and non-lyrical songs have different influences to different emotional states (happiness and sadness in this work). The song stimuli were processed and subjectively annotated with four conditions (i.e., lyrical and non-lyrical songs, and happy and sad emotional states), and the processed stimuli were presented to normal-hearing listeners in ERP experiments according to an affective priming paradigm. The song stimuli served as the prime stimuli and the affective-semantic words as the target stimuli. The experimental results showed that the non-lyrical songs evoked a larger amplitude than lyrical songs for happy emotion, and the lyrical songs evoked a larger amplitude than the non-lyrical songs for sad emotion. Results in this work suggest that the presence of the lyrics has a differential effect in happy and sad songs, and provide evidence that music and lyrics separately influence the diverse emotional processing.

Keywords: Human Performance - Drowsiness and microsleeps, Neurological disorders, Neurorehabilitation
Abstract: In order to reduce motion sickness (MS), a novel vibro-motor reprocessing therapy (VRT), based on an Eye Movement Desensitization and Reprocessing (EMDR) technique is investigated. To the best of the authors knowledge, this is the first time that reprocessing therapy has been evaluated for alleviating MS. Experimentally, MS was induced using visual stimulus of motion videos. Subjective MS was then recorded at baseline for both VRT and non-VRT stimulation conditions, and after each condition, evaluated using a Motion Sickness Assessment Questionnaire (MSAQ). MSAQ scores were compared for both conditions in eight test subjects, with a significant and clear reduction in motion sickness symptoms revealed when applying VRT stimulation. While the subject pool is small, this pilot study indicates that the proposed approach has potential for future exploration in terms of non-pharmacological treatment and management of MS.

Keywords: Human Performance - Ergonomics and human factors, Human Performance - Modelling and prediction, Neural signal processing
Abstract: In previous studies we indicated the electrodermal activity as correlate of subjective motion sickness assessments. To reduce electrodermal activity variations between different measurement sessions of a single subject, we propose a method to scale the electrodermal activity signal on the subjective motion sickness level rated by the subjects. Additionally, our so determined physiological sickness level thus should also simplify individual modelling of motion sickness in cars.

Keywords: Human Performance - Modelling and prediction, Brain Functional Imaging - EEG and Evoked Potentials
Abstract: In this paper, we investigate human decision confidence during image interpretation in an object detection task using electroencephalography (EEG) signals. We develop an EEG dataset acquired from 14 subjects. Five popular EEG features, differential entropy (DE), power spectral density (PSD), differential asymmetry (DASM), rational asymmetry (RASM) and asymmetry (ASM), and two classifiers, a support vector machine (SVM) and a deep neural network with shortcut connections (DNNS), are adopted to measure decision confidence in the object detection task. The classification results indicate that the DE feature with the DNNS model achieves the best accuracy of 47.36% and F1-score of 43.5% for five decision confidence levels. For the extreme confidence levels, the recognition accuracy reaches 83.98%, with an average F1-score of 80.93%. We also found that the delta band performs better than the other four bands and that the prefrontal area and parietal area might be sensitive brain regions that represent decision confidence in object detection tasks.

Keywords: Human Performance - Modelling and prediction, Brain Functional Imaging - EEG and Evoked Potentials
Abstract: To explore the capability of utilizing electroencephalograms (EEGs) for the measurement of human decision confidence levels, this paper develops a new visual perceptual decision confidence experiment. In this experiment, a visual perceptual decision-making task is performed by 14 participants, and their EEG data are recorded. The problem of measuring decision confidence levels is considered to be a pattern classification task, and two pattern classifiers are trained with differential entropy (DE), power spectral density (PSD), differential asymmetry (DASM), rational asymmetry (RASM), and asymmetry (ASM) features extracted from multichannel EEG data. We compare the performance of these features and find that the DE feature performs better than the others for measuring levels of decision confidence. The experimental results indicate that EEG signals offer good capability for measuring human decision confidence levels. The best performance of our proposed method in measuring five levels of decision confidence reaches an accuracy of 49.14% and F1-score of 45.07%, and for the extreme levels of decision confidence, the recognition accuracy reaches 91.28%, with an average F1-score of 88.92%. Topographic maps are also used to depict the neural patterns of EEG signals, suggesting that the posterior parietal cortex and occipital cortex might be sensitive brain areas for indicating decision confidence.

Keywords: Brain Stimulation - Optogenetics, Brain Stimulation-Deep brain stimulation, Brain-computer/machine Interface
Abstract: Neural modulation is a fundamental tool for treating neurological diseases and understanding their mechanisms. One of the challenges in neural modulation includes selecting stimulation parameters, as parameter spaces are very large and their induced effects can exhibit complex behavior. Moreover, the effect of stimulation may depend on the underlying neural state, which can be difficult or impossible to quantify a priori. In this study, we first use an unsupervised learning approach to demonstrate that the effect of medial septum optogenetic stimulation on hippocampal activity differs between awake and anesthetized behavioral states. We then use these data to construct a simulation model of a neural modulation experiment and demonstrate a novel Bayesian optimization method that automatically learns the subject-specific relationship between neural state and its effect on modulation. This approach outperformed standard Bayesian optimization and identified ground-truth optimal parameters of the simulation model, suggesting that this method can efficiently explore complex state-dependent relationships of parameter spaces to improve neural modulation.

Keywords: Brain Stimulation - Sensory restoration, Brain Stimulation - Optogenetics, Neural Interfaces - Recording
Abstract: Stimulation of the visual cortex is a promising method for the restoration of vision, covering a broad range of patients with visual impairments. Optogenetic stimulation has the potential to advance visual restoration. To design better optogenetic implants, an appropriate combination of specific cell types and optogenetic proteins should be identified. Here, we developed a photo-stimulator system and characterized two combinations in the primary visual cortex: targeting excitatory neurons with the red-light-sensitive opto-stimulator chrimsonR and targeting parvalbumin-positive inhibitory neurons with the red-light-sensitive opto-silencer eNpHR-3.0. A custom-developed system consisting of a mini-LED device emitting red-shift light and two convex lenses was installed above the V1 for light focus. We recorded and compared the electrophysiological responses to both visual stimuli and photostimulation. The optogenetic stimulations resulted in a slightly larger number of silence-evoked neurons than excitation-evoked neurons, with a similar number of neurons that responded to visual stimuli.

Keywords: Brain Stimulation-Deep brain stimulation, Brain physiology and modeling - Neuron modeling and simulation, Neural Signal Processing - Time frequency analysis
Abstract: Phase-amplitude coupling (PAC) is a class of cross-frequency coupling that quantifies the degree of synchronization between the phase of a low frequency oscillation and the amplitude of a high frequency oscillation in brain activity. It has been shown that the degree of PAC is associated with normal and abnormal brain functions. Our previous study reported that the transmission of sub-threshold stimuli in a neural network model is improved by High Frequency Stimulation (HFS) with Pulse Frequency-Dependent Resonance (PFDR). In the present study, computer simulations were performed to test the hypothesis that HFS could enhance the degree of PAC in a population of hippocampal CA1 neuron models. The result of the computer simulation show that the Modulation Index (MI) used to evaluate PAC exhibits a typical resonance curve with a maximum value at a pulse frequency of about 160 Hz, implying that HFS at a specific pulse frequency plays a pivotal role in enhancing PAC. This results indicate that PAC could provide a novel way to quantify the effect of HFS to control neural activity.

Keywords: Brain Stimulation-Deep brain stimulation, Neural Interfaces - Neural stimulation, Neural Interfaces - Neural microsystems and Interface engineering
Abstract: Closed-loop deep brain stimulation is a novel form of therapy that has shown benefit in preliminary studies and may be clinically available in the near future. Initial closed-loop studies have primarily focused on responding to sensed biomarkers with adjustments to stimulation amplitude, which is often perceptible to study participants depending on the slew or “ramp” rate of the amplitude changes. These subjective responses to stimulation ramping can result in transient side effects, illustrating that ramp rate is a unique safety parameter for closed-loop neural systems. This presents a challenge to the future of closed-loop neuromodulation systems: depending on the goal of the control policy, clinicians will need to balance ramp rates to avoid side effects and keep the stimulation therapeutic by responding in time to affect neural dynamics. In this paper, we demonstrate the results of an initial investigation into methodology for finding safe and tolerable ramp rates in four people with Parkinson’s disease (PD). Results suggest that optimal ramp rates were found more accurately during varying stimulation when compared to simply toggling between maximal and minimal intensity levels. Additionally, switching frequency instantaneously was tolerable at therapeutic levels of stimulation. Future work should focus on including optimization techniques to find ramp rates.

Keywords: Brain Stimulation-Deep brain stimulation, Neural signal processing, Neurological disorders - Epilepsy
Abstract: Adaptive Deep Brain Stimulation has recently emerged to tackle conventional DBS limitations by measuring disease fluctuations and to adapt stimulation parameter accordingly. In early 2020, Medtronic launched in the European Union the first certified DBS neurostimulator capable of simultaneously stimulate and read signals from the deep brain structures, the PerceptTMPC. In epilepsy, the most common target brain structure is the Anterior Nucleus of Thalamus and the Local Field Potentials analysis requires prior synchronization of data recorded from the Percept PC with video-Electroencephalography (vEEG) equipment. Fine-grained synchronization (sub-second resolution) is mandatory for multimodal neurodata analysis and may be achieved by aligning artefacts perceived in both systems. In this work we study two methods aiming for neurodata streams clock synchronization: one based on DBS stimulation artefacts and another on tapping maneuver artefacts. For this purpose, we studied the data collected from the first epileptic patient that underwent 1-week vEEG-PerceptTMPC monitoring at a Hospital monitoring unit. We found that tapping maneuver-based methodology allowed a more accurate synchronization in relation to the stimulation artefact-based method (0.56s vs. 2.07s absolute average uncertainty). This method was also more complete one since tapping timestamps can be determined by video timeframes and do not require a prior identification of artefacts in EEG data by clinicians.

Keywords: Brain Stimulation-Deep brain stimulation, Neurological disorders, Clinical neurophysiology
Abstract: Deep Brain Stimulation (DBS) is a stimulating therapy currently used to treat the motor disabilities that occur as a result of Parkinson’s disease (PD). The mechanism of how DBS treats PD is poorly understood. Currently, there is a paucity of data from in-vivo human studies on the electromagnetic field (EMF) generated within neural tissue by DBS. In this study, the EMF generated by DBS was analyzed at different distances from the stimulating electrodes. Our goal was to examine how the EMF strength changed with distance in the human brain. The resulting analysis demonstrated differences of several orders of magnitude across the distances measured. With further study, we aim to connect the EMF effect on neural structures to the efficacy of DBS treatment.

Keywords: Brain Stimulation-Deep brain stimulation
Abstract: This paper proposes a three-dimensional (3D) orientation tracking method of a 3D magnetic sensor embedded in a 2.5 mm diameter electrode. Our system aims to be used during intraoperative surgery to detect the orientation of directional leads (D-leads) for deep brain stimulation (DBS) application. The results obtained showed a mean absolute error of 2.46° and a standard deviation of 2.17°. This new approach could be used to detect internal rotation of the electrode during the implantation and to provide relevant information to tune the stimulation parameters accordingly.

Keywords: Neural Interfaces - Implantable systems, Neural Interfaces - Biomaterials, Neural Interfaces - Microelectrode and fabrication technologies
Abstract: Neural recording and stimulation have been widely used to mitigate neurological disorders, such as epilepsy and Parkinson's disease. Current technologies often require invasive surgery and high-risk procedures that hardly overcome the patient’s benefits. A promising strategy designed to overcome these limits involves exploiting the cerebrovascular system as an access route to the neural tissue. Here we present a novel endovascular neural interface, consisting of soft, fully polymeric materials, and degradable in the long-term. This concept can potentially improve the biointegration of the device, reduce inflammatory reaction, allow for implant replacement after its degradation, and avoid removal surgeries. The vasculature's strategic distribution and the adoption of soft polymers in the device structure might allow targeting not only the brain vasculature, but also the peripheral system. Therefore, this novel endovascular neural interface aims at potentially broadening common applications, covering neurological diseases and mental disorders treatments, as well as bioelectronics medicine.

Keywords: Neural Interfaces - Implantable systems, Neural Interfaces - Microelectrode and fabrication technologies
Abstract: Multichannel connectors enable part-replacement of implanted active neural interfaces. For pre-clinical investigation, commercially available miniature connectors enable high channel counts with reduced size and cost. In this paper, Omnetics Nano Circular connectors were encapsulated with medical grade silicone, and assembled using an approach proposed used in surgery. Three 11-pin connectors were tested in PBS for 336 days with cyclic loading for a total of 66 days. A single connector failed with current leakage between channels due to moisture at the connecting interface, and with corrosion at 3 solder joints. The surviving connectors maintained a low contact impedance and high between-channel impedance over 336 days. Inspection of the failed sample emphasizes the need for stress relief near implanted connectors and void-free encapsulation.

Keywords: Neural Interfaces - Implantable systems, Neural Interfaces - Neural microsystems and Interface engineering, Sensory Neuroprostheses - Visual
Abstract: Inductive wireless power transfer empowers microelectronic systems to be implanted in spatially constrained anatomic areas. Due to the need to deliver therapeutic or diagnostic function to sensitive areas, such as the eye, batteries become impractical for reasons of thermal and mechanical safety. In this paper we discuss the requirements and optimization techniques to power an intraocular implant through an inductive coil in the surface of the eye, particularly in the case of a retinal prosthesis. We design and characterize transmitting and receiving inductive coils. Subsequently, through the use of a custom 3D printed mechanical test frame, we characterize the power transfer efficiency at the appropriate geometric parameters. Furthermore, the effect of misalignment, axial displacement, and rotation due to eye movements on power transfer efficiency are quantified in air and through a fragment of animal tissue. These results can be used to model and mitigate movement related power fluctuations.

Keywords: Neural Interfaces - Implantable systems, Neural Interfaces - Neural microsystems and Interface engineering
Abstract: Active neural interfaces for bioelectronic medicine are envisioned to be mm-sized. Such miniaturization is at the moment hampered by the available wireless power techniques as well as the large volume the conventional hermetic packaging adds to the implant. Alternatively, conformal coatings are being explored for the protection of the implant electronics. Such approach has the potential to allow for the use of RF (radio-frequency) energy for powering, and miniaturization to the extreme of having a single IC (integrated circuit) as the whole implant (single chip implants). The longevity of conformal encapsulation can be assessed using accelerated soak tests in a dedicated apparatus in vitro, but these are usually not sufficient, as they fail to reveal additional failure modes that manifest themselves in vivo. Therefore, to investigate the performance of conformal coatings in vivo a compact, mm-sized wireless monitoring system is required. The development of such a system exhibits several challenges, mostly concerned with how, to receive enough energy in such a small implant to power the monitoring sensor and transmit information regarding the integrity of the coating. In this paper proposes a system architecture for such a mm-sized wireless system, suitable for medium-to-long term monitoring of implants, by designing the whole system as a single monolithic IC. It is shown, by experiments, simulation or analytically that the identified challenges are possible to overcome.

Keywords: Neural Interfaces - Implantable systems, Neural Interfaces - Neural stimulation
Abstract: Electrical neuromodulation is an approved therapy for a number of neurologic disease states, including Parkinson’s disease (PD), Obsessive Compulsive Disorder, Essential Tremor, epilepsy and neuropathic pain. Neuromodulatory strategies are also being piloted for an increasing number of additional indications, including Major Depressive Disorder, Dystonia, and addiction. The development of implantable devices capable of both neural sensing and adaptive stimulation may prove essential for both improving therapeutic outcomes and expanding the neuromodulation indication space. Nevertheless, an increasingly fragmented device ecosystem forces researchers and therapy developers to customize and reinvent data visualization, clinician engagement, and device control software to support individual clinical studies. Each hardware platform provides a unique software interface to the implanted neurostimulator, making pre-existing code from prior studies difficult to leverage for future work -- a hindrance that will expand as device technology diversifies. Here, we envision, detail, and demonstrate the use of a novel software architecture, OMNI, that accelerates neuromodulation research by providing a flexible, platform- and device-agnostic interface for clinical research and therapy development.

Keywords: Neural Interfaces - Implantable systems, Neural Interfaces - Neuroimaging, Neural Interfaces - Sensors and body Interfaces
Abstract: Real-time monitoring of the spinal cord was achieved on pig model with custom devices operating in both reflection and transmission modes. Although the reflective system was generally preferred, the obtained data wasn’t as rich as in transmission and could conduct to erroneous conclusions.

Keywords: Neural Interfaces - Implantable systems, Neural Interfaces - Recording, Neural Interfaces - Regeneration and tissue-electrode Interface
Abstract: Microneedle nerve array (MINA) is a high-density electrode array built on an ultra-flexible substrate with a monolithically integrated stretchable cable. The axon-sized electrodes and cuffless implantation approach were specially designed for reducing tissue reactivity and improving spatial fidelity in small nerves. Acute multi-channel recordings from a rodent peroneal nerve are summarized. Long-term reactivity was also measured using cuff-free, non-electrical MINAs implanted for 1 to 6 weeks on the rat vagus nerve. Cuffless attachment of MINA was achieved using an innovative photochemical tissue bonding technology with rose bengal. We used micro-CT scanning to measure needle to nerve displacement at 1 and 6 weeks with < 4-μm accuracy. Histomorphology studies quantified the axon loss after both sham and MINA implantation procedures of nerve sections proximal and distal to the implant.

Keywords: Neural Interfaces - Implantable systems, Neural Interfaces - Recording, Neural signal processing
Abstract: Peripheral nerve recording interfaces are a new frontier in neuroprosthetic applications. Nevertheless, an integrated medical device offering both electroneurographic (ENG) signal sensing and decoding is still missing. This paper aims to summarize the process of integrating existing technologies into a full-stack recording device. To this end, the system requirements are provided, together with a description of the building blocks that compose a recording interface: electrode, acquisition system, classification algorithm, power and communication units. The core of our contribution is a detailed analysis of the unsolved conflicts which arise during the assembling process, followed by the proposal of a few compromise solutions.

Keywords: Neural Interfaces - Implantable systems, Neural signal processing, Neurological disorders - Epilepsy
Abstract: A novel approach to the treatment of drug-resistant patients with epilepsy involves the use of implantable devices that deliver electrical stimulation to the epileptic focus at seizure onset. Accordingly, this process requires reliable and energy-efficient seizure detection. To this end, first, for finding the best match between the electrode configuration of an implantable device and the layout of electrodes used during long-term recordings for epilepsy diagnostics, we designed two automatic electrode selection methods. We next implemented four seizure detection algorithms, namely Random Forest (RF), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and Convolutional Neural Network (CNN). We compared their performance using the automatically selected electrodes. The proposed CNN model showed the best performance, with a mean AUC-ROC (area under the receiver operating characteristic curve) score of 0.94. These results were obtained by applying just four channels with a limited spatial distribution. Therefore, automatic electrode selection methods enable an optimal training of the seizure detection algorithm. Besides, our newly designed seizure detection algorithm is a promising candidate for application in implantable devices.

Keywords: Neural Interfaces - Implantable systems, Neurological disorders, Neural Interfaces - Microelectrode and fabrication technologies
Abstract: Cortical spreading depolarizations (SDs) are pathologic disruptions in brain activity following a variety of insults such as traumatic brain injury (TBI) and stroke. Here, we show for the first time the ability to record SDs using a soft thin-film microelectrode array technology. The device is placed subdurally in a rat model where SDs are induced using KCl solution droplets applied to the cortex. These devices will be useful in deciphering the underlying mechanisms of SDs and ultimately once transferred to the clinic in treating patients suffering from TBI.

Keywords: Neural signal processing, Neurological disorders - Stroke, Brain Functional Imaging - NIR
Abstract: Since stroke is still the leading cause of death worldwide, neuroimaging research to investigate the mechanism under the brain reorganization and rehabilitation is becoming predominant. In such areas, lesion delineation is a critical step and manual tracing is the gold standard so far, but it is time-consuming and readily influenced by bias. In this work, we propose a volumetric convolutional neural network for lesion segmentation with a novel and well-motivated Hierarchically Spatial Encoding Module (HSEM) based on Recurrent Neural Network (RNN). The proposed HSEM leverages the inherent spatial characteristics of the human brain by encoding the context from various dimensions hierarchically, therefore enhancing the segmentation results. We evaluate our approach on a fresh-new benchmark data set ATLAS. Our method achieves the F1-score of 66.07%, precision of 69.32%, recall of 67.6%, and obtains superior performance compared with other state-of-the-arts. The experiments prove that our proposed module has strong robustness when embedded into the neural network structure and further verify the module can mimic the human brain to make a diagnostic decision with the existence of inherent anisotropy and spatial properties. Furthermore, our model has the potential to provide finer lesion segmentation in some cases where the lesion is not annotated by experts perfectly.

Keywords: Neurorehabilitation, Human Performance - Cognition, Neurological disorders
Abstract: Alzheimer’s disease (AD) is the most common cause of dementia among the elderly and it slowly reduces cognitive and physical skills. Currently, only assistive apps are available on the market, while no-customizable therapeutic tools exist to fight AD symptoms. An innovative mobile app, named REACT, is presented as a fully customizable mobile app, with the goal to act against the progression of AD.

Keywords: Neurorehabilitation, Human Performance - Modelling and prediction
Abstract: Quantitative assessment of motor disorder is one of the main challenges in the field of stroke rehabilitation. This paper proposes a simplified kinematic model for human upper limb(UL) using seven main joints of both the dominant and non-dominant side. With this model, a deep neural network (DNN) is used to predict the 3D free reaching movement of UL of a healthy participant. The experimental results show that the prediction trajectories can achieve high similarities with trajectories of real movements, indicating the promising accuracy in 3D movement estimation of UL achieved by the DNN. With the capability of identifying specific reaching movements in real-time, the trajectories predicted by this data-driven model can be utilized to inform the rehabilitation assessment and training in the future studies as a personalized therapy approach.

Keywords: Neurorehabilitation, Neurological disorders, Neural Interfaces - Neural stimulation
Abstract: Spinal cord injury results in neurogenic lower urinary tract dysfunction (NLUTD) leading to reduced bladder capacity, loss of bladder sensation and inability to initiate bladder voiding. Transcutaneous Electrical Spinal Cord Neuromodulation (TESCoN) was developed to activate and retrain the spinal circuits to enable function. In this pilot study, we developed a novel method to retrain the bladder by externally filling and voluntarily emptying the bladder with saline in the presence of TESCoN. We successfully demonstrated the use of TESCoN in improving bladder capacity, regaining sensation and enabling voluntary voiding with increased voiding efficiency in an individual with complete chronic paralysis.

Keywords: Neurorehabilitation
Abstract: Use of standardized and scientifically sound outcome measures is encouraged in clinical practice and research. With the development of newer rehabilitation therapies like Robotic therapy and Functional Electrical Stimulation we need technology-supported upper extremity outcome measures that are easily accessible, reliable, and valid. We developed a 3D printed upper extremity outcome measure called the Toronto Rehabilitation Institute – Hand Function Test and assessed its psychometric properties in Stroke and Spinal Cord Injury population. We found the 3D printed test to be reliable and valid in both populations.

Keywords: Neurorehabilitation - Robotics, Neurorehabilitation - Wearable systems, Human Performance - Gait
Abstract: Robot-aided gait rehabilitation needs to be fruitful from a motor learning perspective, hence optimal human-robot interaction strategies are necessary. Therefore, we performed a systematic search on the effects of different control algorithms on quantitative objective gait parameters of post-acute stroke patients. 31 studies met the inclusion criteria and the preliminary analysis show no clear feedback controller preference with respect to gait parameters improvements. Also, the heterogeneity of performance assessment and patients numerosity put at risk the possibility to conduct a rigorous meta-analysis.

Keywords: Neurorehabilitation - Virtual reality, Human Performance - Cognition
Abstract: Virtual Reality (VR) is one of the most prevalent interdisciplinary branches of technology where a human being can communicate with the components of virtual environment. It has been serving as a successful assistive technology for several disordered conditions. Children with neuro developmental disorders have the inadequacy to learn new things or adapt to changes in the environment. Although, there are various conventional techniques like flashcards, blackboard, parental care etc., available to train such children,VR has been found to bring promising outcomes in terms of behavior and learning. In this study, different virtual environments have been developed and experimented with a sample group of normal and autistic children to test the cognitive learningability. Correspondingly, EEG signal acquisition and evaluationhas been performed which showed significant improvement in their cognitive learning ability during VR therapy.

Keywords: Neurorehabilitation - Virtual reality, Neurological disorders, Human Performance - Cognition
Abstract: Freezing of Gait (FoG) is a common condition in patients with Parkinson's disease (PD). Neural mechanism of FoG is not well-known, and its treatment is a clinical challenge. Here, we propose a setup integrating a leg-press robotic device and an immersive virtual reality (VR), allowing to simulate walking in a sitting position. Our approach can contribute to implementing future rehabilitation strategies for FoG.

Keywords: Neurorehabilitation - Wearable systems, Human Performance - Fatigue, Neuromuscular Systems - Wearable systems
Abstract: In this work, a new fully embedded wearable device for the continuous measurement of respiratory variables via Fiber Bragg Grating (FBG) sensors is introduced. An experimental protocol for the assessment of its performance in the monitoring of breathing activity and detection of physical fatigue is proposed as well.

Keywords: Neurorehabilitation - Wearable systems, Motor Neuroprostheses - Prostheses, Neuromuscular Systems - EMG models, processing and applications
Abstract: Poly-articulated, myoelectric hand prostheses reproduce complex multi-degree of freedom movements. Typically, a pattern recognition algorithm translates the recorded electromyographic (EMG) activity into joint movements. Control algorithms may benefit by adding more EMG sensors, however, their mechanical integration within the socket strongly affects the physical robustness of the prosthetic system. Their typical size and rectangular shape are indeed non-optimal for the limited amount of space within the socket. To solve this issue, here we present and test custom-made sensors for decoding multi-joint hand movements from EMG recordings of arm muscles. The sensors have circular shape and smaller size with respect to their standard counterpart, thus allowing a higher number of channels for multi-DOF control strategies. In order to evaluate their performance for multi-joint decoding, we tested a Non-Linear Logistic Regression classifier on both healthy and amputated subjects. We optimized the classifier in terms of F1Score, depending on the number of EMG sensors, and in terms of Embedding Optimization Factor, depending on the polynomial complexity degree. We then compared performance with that of standard rectangular EMG sensors and found no significant difference. Our custom-made sensors achieved higher F1Score for all the patients. This result, coupled with more effective integration with the socket, suggests effective prosthetic applications for our sensors.

Keywords: Neurorehabilitation - Wearable systems, Neural Interfaces - Neural stimulation, Motor Neuroprostheses - Neuromuscular stimulation
Abstract: In this paper, a wearable, battery-powered, multichannel miniature electrical stimulator is developed. The stimulator consists of power module, controller module, bidirectional constant-current module and channel multiplexing module. The stimulator circuit can output maximum ±10 mA constant-current stimulation with a compliance voltage of 80 V, at a 5 V supply voltage. The size of the prototype is 35 × 35 × 22 mm3 and can be easily integrated into the socket of a prosthetic hand. The system is capable of providing biphasic pulse stimulation to six channels simultaneously with programmable amplitude, pulse width and frequency or outputting arbitrary continuous waveform on a single channel. The comparable performance of our stimulator to similar commercially available and developed devices is proposed. The stimulator can be used for providing the sensory feedback of neural hand prostheses as well as electrical functional stimulation for kinds of sensorimotor rehabilitation.

Keywords: Neurorehabilitation - Wearable systems, Sensory Neuroprostheses - Somatosensory and vestibular, Human Performance - Sensory-motor
Abstract: Walking confidence of lower limb amputees would improve if the prosthesis could provide information about terrain regularity. Here, a neuromorphic augmenting feedback enabling identification of terrain is tested in a pilot experiment with a transtibial amputee. Results show a >89% accuracy in discriminating even and uneven terrains.

Keywords: Neurological disorders - Stroke, Human performance, Human Performance - Modelling and prediction
Abstract: The understanding of the underlying neurobiology of behavioral dysfunction provides significant insights in therapeutic intervention planning in stroke. There have been a variety of lesion-behavior mapping studies to relate the brain injury location to neurological symptoms. However, these studies were mostly focused on the relationship between stroke lesion location with a specific behavioral deficit domain, based on a limited sample size. In this study, we investigated the relationship between stroke lesion distribution and behavior deficits based on a large stroke patient cohort (N=632), with neurological symptoms spanning across all representative domains. We divided the data into five clusters of behavioral deficits, including cognitive, motor, somatosensory, visual and limb ataxia groups, based on the NIHSS sub-scores using the hierarchical agglomerative clustering algorithm. The lesion distribution in major blood supply territories were investigated and lesion loads in the high injury incidence area were compared across different behavior deficits domains of patients. The middle cerebral artery (MCA) territory contained the most lesions and the cognitive deficit group had the highest lesion rate in MCA. The lesion loads of basal ganglia and internal capsule anterior limb were higher in patients with visual or cognitive deficits. Our study of lesion characteristics might shed light on the understanding of the lesion-behavior relationship in ischemic stroke patients.

Keywords: Neuromuscular Systems - EMG models, processing and applications, Neural signal processing, Human Performance - Modelling and prediction
Abstract: A loss of individuated finger movement affects critical aspects of daily activities. There is a need to develop neural-machine interface techniques that can continuously decode single finger movements. In this preliminary study, we evaluated a novel decoding method that used finger-specific motoneuron firing frequency to estimate joint kinematics and fingertip forces. High-density electromyogram (EMG) signals were obtained during which index or middle fingers produced either dynamic flexion movements or isometric flexion forces. A source separation method was used to extract motor unit (MU) firing activities from a single trial. A separate validation trial was used to only retain the MUs associated with a particular finger. The finger-specific MU firing activities were then used to estimate individual finger joint angles and isometric forces in a third trial using a regression method. Our results showed that the MU firing based approach led to smaller prediction errors for both joint angles and forces compared with the conventional EMG amplitude based method. The outcomes can help develop intuitive neural-machine interface techniques that allow continuous single-finger level control of robotic hands. In addition, the previously obtained MU separation information was applied directly to new data, and it is therefore possible to enable online extraction of MU firing activities for real-time neural-machine interactions.

Keywords: Brain Stimulation - Transcanial magnetic stimulation (TMS), Brain Functional Imaging - EEG and Evoked Potentials, Neural signal processing
Abstract: Transcranial magnetic stimulation (TMS) with concurrent electroencephalography (EEG) is a key tool towards non-invasively characterizing causal circuits in humans. However, the recorded data is highly noisy due to a variety of stimulation-induced artifacts and other noise sources. While many TMS-EEG processing pipelines require manual intervention, the pipeline described here is fully automated. As part of this pipeline, we introduce several novel approaches specifically designed to mitigate TMS-induced artifacts to produce cleaner and more reliable TMS-evoked EEG responses.

Keywords: Brain Stimulation - Transcanial magnetic stimulation (TMS), Brain physiology and modeling, Brain Functional Imaging - fMRI
Abstract: Patients with schizophrenia often receive rTMS treatment, in which the dorsolateral prefrontal cortex (DLPFC) or the primary auditory cortex and the temporo-parietal junction are stimulated to treat cognitive deficits or auditory hallucinations, respectively. In this pilot study, we used individualized finite element models derived from patients’ MRIs to investigate the correlation between RMT and brain anatomy. We first developed anatomically accurate head and brain models of schizophrenia patient MRIs using SimNIBS pipeline. We then utilized finite element analysis software to compute induced electric fields. The electric field induced in the brain was recorded and compared with variables such as cortex-coil distance (CCD), age, and RMT. Our results show that there is little to no correlation between the measured CCD at M1 and the measured RMT or the maximum electric field recorded in the brain after TMS was simulated using Sim4Life. Further, we examined the relationship between RMT and a resting-state fMRI metric of healthy brain function – the default mode-task network connectivity and found that stronger anti-correlations of the default and task nodes were associated with higher RMT, albeit not meeting statistical significance. Thus, we hypothesize that the lack of clear correlation between the CCD at M1 and the measured RMT and the possible negative correlation between RMT and default-task node connectivity suggests that there are other variables that influence RMT.

Keywords: Brain Stimulation - Transcanial magnetic stimulation (TMS), Brain Stimulation - Transcranial direct current Stimulation (tDCS), Brain physiology and modeling
Abstract: The Boundary Element Fast Multipole Method for computational electromagnetic modeling of transcranial neurostimulation methods is applied to the high-resolution MIDA human head model to investigate the effects of cancellous bone and dura mater on transcranial magnetic stimulation (TMS) and transcranial electrical stimulation (TES). After validating the model and method against commercial electromagnetic software, we show that the cancellous bone of the skull and the dura mater have a negligible effect on TMS but a substantial effect on TES. Further investigation shows that the electric field induced by TES at the inner cortical surface for a simplified model might be proportional to the electric field at the same location for the full model, but this simple scaling operation likely does not hold for the outer cortical surface.

Keywords: Brain Stimulation - Transcranial direct current Stimulation (tDCS), Brain physiology and modeling - Neuron modeling and simulation, Neurorehabilitation
Abstract: Transcranial Direct Current Stimulation is a non-invasive brain stimulation that modulates brain activity and enhances post-stroke recovery. However, TDCS effects are highly variable, which might be caused by the difference in individual electric fields. We constructed individualized volume conductor models and performed electric field simulations TDCS for 24 stroke subjects. Correlation analysis was performed to identify associations between electric field and lesion properties. Significant correlations were observed between the field strength and lesion-anode distance (r=-0.61,p=0.003) and lesion-target distance(r=-0.59, p=0.005). Subsequent analysis found that distance between the stimulation hotspot and target was positively correlated with lesion-hotspot distance (r= 0.58,p=0.004) and anode-hotspot distance (r=0.94, 0.000). Our results shows possible tunneling effects induced from stroke lesions which increases the stimulation intensity to its nearby volumes. With heterogeneous lesion properties for stroke individuals, more efforts are required to design an optimal stimulation montage for chronic stroke.

Keywords: Brain Stimulation - Transcranial direct current Stimulation (tDCS), Neurological disorders - Epilepsy, Brain Functional Imaging - Source localization
Abstract: The principle of epilepsy surgery is to localize and to resect the epileptogenic zone (EZ) but surgery might not be feasible when the focal cortical dysplasia (FCD) is overlapped with eloquent areas of the brain. Multi-channel transcranial electric stimulation (mc-TES) has been proposed recently as a neurotherapeutic approach. However, the selection of the individual head model is crucial to achieve accurate optimized mc-TES in the FCD. Here, we evaluate simulations of optimized TES montages when using different individual head models.

Keywords: Brain Stimulation - Transcranial direct current Stimulation (tDCS), Neurological disorders - Epilepsy
Abstract: Cathodal transcranial direct current stimulation (c-tDCS) is a non-invasive option for treatment of refractory epilepsy. However, it is still unknown whether this therapy has a positive stabilizing effect on the vegetative function of these patients. Heart Rate Variability (HRV) is considered an efficient tool to monitor the cardiac autonomic system, which has been correlated with the risk of Sudden Unexpected Death in Epilepsy (SUDEP). In this study, changes in HRV are investigated after c-tDCS of six patients (34.50 ± 11.10 years) with refractory epilepsy, which have been selected at the University Hospital, LMU Munich. Patients were categorized as responders (n=2), non-responders (n=3) and uncategorized (n=1). We analyzed 24 hours of electrophysiological data recorded before and after treatment, and computed HRV metrics (AVNN, SDNN, RMSD, pNN20, pNN50, LH/HF, 0V, 1V, 2LV, 2UV, SD1 and SD2). All patients revealed a change in almost all HRV metrics post stimulation. Grouped all patients, there was a significant (p<0.05) change in RMSSD, pNN50, SD1 and LH/HF. For responders there was an increase in all time domain and non-linear metrics, which was not seen for non-responders. These results suggest that tDCS exerts significant changes in cardiovascular autonomic system in patients with refractory epilepsy. HRV metrics may also serve as biomarkers of the response to tDCS stimulation. A larger dataset is being gathered for further analysis.

Keywords: Brain Stimulation - Transcranial direct current Stimulation (tDCS), Neuromuscular Systems - Neurorehabilitation, Neurological disorders - Stroke
Abstract: Transcranial alternating current stimulation (tACS) is an emerging non-invasive neuro-modulation technique which has been proved to enhance neuro-rehabilitation in stroke. Simultaneous acquisition of neuroimaging data with brain stimulation allow a noninvasive investigation of the brain dynamic changes during the process. In the concurrent tACS-fMRI study, fMRI data were collected in 13 chronic stroke individuals under different tACS protocols (10 Hz, 20 Hz and sham). Resting-state fMRI was acquired before, during and after the stimulation to investigate the dynamic process of neural response. In this preliminary analysis, ICA across conditions and subjects was used to extracted typical brain networks. The extracted networks were used as masks to do network to network functional connectivity analysis. Functional connectivity between networks was found to be significantly modulated for default-mode network, contralesional fronto-parietal network and executive control network, and this modulation effect varied for different stimulation frequency. 10 Hz tACS exhibited stronger modulation effects in the brain network interactions.

Keywords: Brain Stimulation - Transcranial Ultrasound Stimulation (TUS), Neurological disorders
Abstract: Vascular dementia (VD) is a kind of senile dementia with cognitive impairments. Working memory impairment is one of the remarkable symptoms after VD, while there is still no effective treatment. Due to the good penetrability and high spatial resolution, low-intensity focused ultrasound stimulation (LIFUS) has been recognized as a potential noninvasive therapeutic method in nervous system diseases. Whether LIFUS could reverse the working memory impairment is still unknown. Thus in this study, we tested the neuroprotective effects of 0.5 MHz LIFUS with a pulse repetition frequency of 2.0 kHz; and spatial peak temporal average intensity of 500 mW/cm2 at duty cycle of 20% on VD-induced working memory impairment. Our research suggested that the VD model impaired the spatial working memory and recognition memory, while 2-week LIFUS treatment could some extent improve spatial working memory driven by spontaneous alternation or rewards. Thus, these findings indicated that LIFUS might become a potential therapy for VD in the future.

Keywords: Brain Stimulation - Transcranial Ultrasound Stimulation (TUS), Brain Stimulation - Transcranial direct current Stimulation (tDCS), Neural Interfaces - Neural stimulation
Abstract: Despite the recent advances in direct brain stimulation techniques, improving the spatial resolution of non-invasive brain stimulation is still a challenge. Here, we propose a dual-modality method with an improved spatial resolution which combines two noninvasive direct brain stimulation methods: (1) transcranial focused ultrasound stimulation and (2) transcranial direct current stimulation. Using this dual-modality approach, we demonstrate the first study in mice by targeting the motor cortex and eliciting motor responses. Our results show that successful stimulation was possible at a lower intensity threshold compared to using each modality separately. This study on improving the spatial resolution of direct brain stimulation using a noninvasive dual-modality approach presents a promising new therapeutic method.

Keywords: Neuromuscular Systems - Peripheral mechanisms, Neuromuscular Systems - EMG models, processing and applications, Neuromuscular Systems - Learning and adaption
Abstract: Sensory receptors within joints provide the central nervous system (CNS) with information about the stress and strains in joint structures such as ligaments and menisci. How these joint sensory afferents are used by the CNS to generate motor commands is still poorly understood. In this study, we evaluate the hypothesis that the role of joint sensory afferents depends on the level of joint loading. To this end, we assessed the effect of temporarily inhibiting joint sensory receptors on quadriceps muscles activity during locomotion in the rat before and after paralysis of vastus lateralis, a perturbation that causes medial loading in the patellofemoral joint. The CNS compensated for the loss of vastus lateralis by gradually increasing the activity of vastus intermedius and rectus femoris over five weeks of adaptation. This strategy limits patellofemoral joint loading, suggesting that the CNS regulates internal joint stresses and strains. However, the temporary inhibition of knee sensory receptors did not cause significant changes in quadriceps muscle activity, both before and at any time point after paralysis. We therefore found no evidence for the existence of fast feedback loops mediated by joint sensory afferents, that depends on patellofemoral joint loading. Additional work is needed to investigate whether joint sensory afferents mediate long-term adaptation to joint stresses and strains.

Keywords: Neuromuscular Systems - Locomotion, posture and balance, Brain Functional Imaging - Connectivity and Network, Neuromuscular Systems - EMG models, processing and applications
Abstract: Muscle synergy assessments are often employed to evaluate the modular organization of the spinal cord during a locomotion task. While they provide valuable insights into the pattern formation of the alpha-motoneurons at the spinal cord, by construction they cannot capture control from supra-spinal layers. We examined how locomotor muscle synergies are represented in the sensorimotor cortex, with particular focus on the cortico-synergy coherence as a measure of coupling along the cortico-spinal tract. Non-negative matrix factorization served to decompose multivariate electromyographic signals into muscle synergies. Their representations were localized in the cortex using coherence-based beamforming. Overall, the cortico-synergy coherence was maximal in sensorimotor areas especially in the beta-frequency band. However, only for the synergies timed to heel strike, that are related to the double support phases, the coherence was significant. These coherences were closely related to the timing of the activation patterns of the synergies, suggesting sensorimotor cortex to be strongly involved in emergence and control of these synergies.

Keywords: Neuromuscular Systems - EMG models, processing and applications, Motor Neuroprostheses - Prostheses
Abstract: The control of robotic prosthetic hands (RPHs) for upper limb amputees is far from optimal. Simultaneous and proportional finger control of a RPH based on EMG signals is still challenging. Based on EMG and kinematics recordings of subjects following a pre-defined sequence of single and multi-fingers movements, we aimed at predicting finger flexion and thumb opposition angles. We compared two deep learning (DL) based approaches, the first one using the raw EMG signals and the second one using the spectrogram of the signal as input, with the standard state of the art decoding technique (STD) for finger angle regression. Using a genetic algorithm for hyper-parameter optimization, we obtained an optimized model architecture (and set of features in the case of STD) for each condition on one recording session. Then, we evaluated the best model of each condition on the eleven EMG and finger kinematics recordings available from four subjects. The two DL approaches based on convolutional neural networks predicted finger angles with a similar mean squared error loss but both of them outperformed the standard approach for the regression of simultaneous single-finger angles. This proposed decoding strategy and hyper-parameter optimization framework provides a basis to further improve single finger proportional control for RPHs.

Keywords: Motor learning, neural control, and neuromuscular systems, Neuromuscular Systems - Neurorehabilitation, Neurological disorders
Abstract: This paper presents a recursive proportional-integral-derivative (rPID) controller for automatically adjusting M-wave size during H-reflex operant conditioning. H-reflex operant conditioning is a recently developed rehabilitation method for the recovery of movement following spinal cord injury and other neuromuscular disorders. During this protocol, researchers and clinicians manually control the M-wave size—an electromyographic (EMG) indicator of electrical stimulation intensity. Here, we compare the present method for manually controlling M-wave size to two versions of the rPID controller (rPIDβ=0.45 and rPIDβ=0.95) during H-reflex operant conditioning trials of the flexor carpi radialis. Initial experiments with three participants show that the manual controller and the rPIDβ=0.95 controller successfully maintained M-wave size with similar success rates, mean squared errors, and number of trials before the first trial within ±20% of the target M- wave size. Our new automated system—with further testing and optimization—will allow researchers and clinicians to focus more on participants, reducing the amount of training they require to perform H-reflex operant conditioning, and potentially improving the efficiency and efficacy of H-reflex operant conditioning protocols.

Keywords: Motor learning, neural control, and neuromuscular systems, Motor Neuroprostheses - Neuromuscular stimulation
Abstract: Although vagus nerve stimulation (VNS) can be used to reduce heartrate by enhancing parasympathetic activity, a fully controllable intervention would also require a method for downregulating parasympathetic activity. A direct current (DC) block can be applied to a nerve to block its action potential conduction. This nerve block can be used to downregulate parasympathetic activity by blocking afferent reflexes. The damaging effects of reactions that occur at the electrode-nerve interface using conventional platinum electrodes can be avoided by separating the electrode from the nerve. Using a biocompatible, ionically conducting medium, the electrode and the damaging reactions can be isolated in a vessel away from the nerve. This type of electrode has been called the Separated Interface Nerve Electrode (SINE). Fuzzy logic control (FLC) is a controller approach that is well suited to physiological systems. The SINE, controlled by an FLC, was utilized to block a stimulated vagus nerve and regulate heart rate. The FLC was able to maintain the heartrate at a pre-determined setpoint while still achieving instant recovery when the block was removed.

Keywords: Sensory Neuroprostheses - Somatosensory and vestibular, Human Performance - Cognition, Neural Interfaces - Sensors and body Interfaces
Abstract: Proprioception provides information regarding the state of an individual’s limb in terms of static position and kinesthesia (dynamic movement). When such feedback is lost or impaired, the performance of dexterous control of our biological limbs or assistive devices tends to deteriorate. In this study, we determined if external vibratory stimulation patterns could allow for the perception of a finger’s static position and kinesthesia. Using four tactors and two stimulus levels, eight vibratory settings corresponded to eight discrete finger positions. The transition patterns between these eight settings corresponded to kinesthesia. Three experimental blocks assessed the perception of a finger’s static position, speed, and movement (amplitude and direction). Our results demonstrated that both position and kinesthesia could be recognized with over 93% accuracy. The outcomes suggest that vibratory stimulus can inform subjects of static and dynamic aspects of finger proprioception. This sensory stimulation approach can be implemented to improve outcomes in clinical populations with sensory deficits, and to enhance user experience when users interact with assistive devices.

Keywords: Neural Interfaces - Neural microsystems and Interface engineering, Neural Interfaces - Neural stimulation, Neural Interfaces - Recording
Abstract: Reliable bidirectional neural interfaces are crucial for exploiting the full potential of neural prostheses. Despite recent advancements, current microelectrode technologies evidence important shortcomings. This paper summarizes our efforts to explore novel technologies based on graphene and graphene-related materials

Keywords: Neural Interfaces - Biomaterials, Neural Interfaces - Regeneration and tissue-electrode Interface, Neural Interfaces - Microelectrode and fabrication technologies
Abstract: Approaches aimed at addressing the mechanical and electrical mismatch between devices and the nervous system include use of organic materials, mixed conductors and tissue engineering technologies. While organic conductors improve the electrochemical performance for stimulating and recording, they have minimal impact on the mechanical mismatch. The use of hydrogels can reduce inflammation while not impacting electrical performance. Future designs seek to change the mechanism of stimulation through using stem cell technologies paired with bionic devices to create living bioelectronics.

Keywords: Neural Interfaces - Biomaterials, Neural Interfaces - Microelectrode and fabrication technologies, Neural Interfaces - Recording
Abstract: Low-impedance electrode coatings show great potential for enhancing the performance of neural probes by improving the signal to noise ratio and thus the overall recording quality. While impedance metrics of such materials are typically evaluated in vitro, it is their chronic performance in vivo that eventually matters for the final applications. In our study we therefore address the question whether low impedance as measured in vitro can serve as proxy to predict high quality recordings in vivo.

Keywords: Neural Interfaces - Implantable systems, Neural Interfaces - Neural stimulation, Neural Interfaces - Neural microsystems and Interface engineering
Abstract: Advances in electrodes hold the potential to improve the stimulation safety for implantable electrical stimulators, but electrode developers should understand some electronics design to optimise overall device safety.

Keywords: Neural Interfaces - Microelectrode and fabrication technologies, Neural Interfaces - Biomaterials, Neural Interfaces - Implantable systems
Abstract: Glassy carbon (GC) has gained interest as an electrode material for neural implants because it naturally serves as a biocompatible multi-functional material, able to record and stimulate neural activity but also to detect bio-species in the brain. The combination of these properties could potentially improve the performance of neural implants, by creating a closed-loop system that modulates the stimulation protocols based on the feedback provided by electrophysiological recordings and bio-detection. However, the use of GC as an electrode material for thin-film implants definitely comes with some challenges. The carbonization process itself and the incorporation of GC within flexible substrates are among them, and - many device iterations later - we can provide our insights on how to overcome those challenges.

Keywords: Neural Interfaces - Neural stimulation, Neural Interfaces - Implantable systems, Neural Interfaces - Biomaterials
Abstract: In this talk the dogmas of charge balanced stimulation will be challenged, based on long experience of stimulation with platinum and stainless steel.

Keywords: Neural Interfaces - Microelectrode and fabrication technologies, Neural Interfaces - Neural stimulation, Neural Interfaces - Biomaterials
Abstract: Metal oxides with a range of cation valence states are candidates as charge-injection coatings for neural stimulation electrodes. The physical and electrochemical properties of valence-change oxides relevant to their use as high charge-injection capacity microelectrode coatings are discussed. The properties of iridium oxide coatings, the prototypical oxide currently employed in animal and some clinical studies, are used to demonstrate the salient features of valance-change oxide electrodes. The properties of ruthenium oxide, which is another multi-valent oxide are also presented and used to highlight the behavior of oxide electrodes.

Keywords: Brain-computer/machine Interface, Motor Neuroprostheses - Neuromuscular stimulation, Human Performance - Sensory-motor
Abstract: After sustaining a spinal cord injury (SCI) volitional control and sensory feedback of areas below the level of injury are often diminished. These deficits present many challenges to living an independent lifestyle. Traditional methods of rehabilitation show modest improvement of function, however, individuals living with SCI demand increased control and communication with their bodies. Cutting edge translational research has shown potential for this to occur although there are many considerations for all stakeholder groups to address during the development of equipment or therapies.

Keywords: Neurorehabilitation, Neural Interfaces - Neural stimulation, Brain Stimulation - Transcranial direct current Stimulation (tDCS)
Abstract: While spinal cord injury results in loss or impairment of motor function below the level of the lesion, many individuals have some sparing of spinal pathways. In some individuals, practice and training can improve functional use of the upper and lower extremities even years after injury. Activating the nervous system via non-invasive stimulation of cortical or spinal neural circuits, or peripheral nerves can be a valuable adjunct to training.

Keywords: Brain Stimulation - Sensory restoration, Sensory Neuroprostheses, Brain-computer/machine Interface
Abstract: Brain computer interfaces offer the opportunity for individuals with high-level tetraplegia to regain hand and arm function. Bidirectional systems provide sensory feedback from the hand to the user to enable more intuitive control and a more natural user experience.

Keywords: Neural Interfaces - Recording, Brain Stimulation-Deep brain stimulation, Brain-Computer/Machine Interface - Biofeedback
Abstract: Improved understanding of the brain processes underlying normal and abnormal function is necessary for devising better ways to diagnose, alleviate, or cure neurological or psychiatric disorders. It is clear that even for simple behaviors, such processes depend on interactions among multiple brain regions. However, these interactions themselves are less well understood. This inadequate understanding of inter-regional interactions impedes the generation of substantive models of brain functions and the new diagnostic or therapeutic possibilities that such models could introduce. These deficiencies reflect in part the limitations of the widely used imaging modalities. Detailed analysis of the operation of a network of brain regions requires comprehensive coverage, high spatial resolution, and high temporal resolution. However, existing techniques either lack high temporal resolution, high spatial resolution, or comprehensive coverage. Thus, they cannot track the spatial and temporal progression of inter-regional interactions. Intracranial recordings using electrocorticographic (ECoG) electrodes placed on the brain surface, or depth electrodes (stereoencephalography; SEEG) placed in regions and sulcal depths not accessible with ECoG, can provide wide coverage and high temporal and spatial resolution. Furthermore, electrical stimulation through these electrodes can assess causal roles and inter-regional connections. Chronic human intracranial data are currently (and for the foresee

Keywords: Neural Interfaces - Neuroimaging, Brain physiology and modeling, Brain Functional Imaging - Connectivity and Network
Abstract: Normal and pathological networks related to seizure propagation contributes to diagnosis and surgical monitoring in epilepsy treatment. Since focal and generalized epileptogenic syndromes abnormalities might involve multiple foci and large-scale networks, we applied electrophysiolpgy (cortco-cortico evoked potential; CCEP) and tractography to make detailed diagnosis for complex syndrome. All 17 epilepsy patients with no or little abnormality on images investigations underwent subdural grid implantation for epilepsy diagnosis. To perform quick network analysis, we recorded and analyzed high gamma activity (HGA) of epileptogenic activity and CCEPs to identify pathological activity distribution and network connectivity. [Results] Pathological CCEPs showed 2 negative deflections consisting of early (less than 40 ms) and late (less than 150 ms) components in electrically stable circumstance at bed side and early CCEPs appeared in 57% of the patients. Early components of pathological CCEPs diminished after complete disconnection of tractoography-based fibers between the foci in 7 of 8 cases. One case with residual pathological CCEPs showed poorer outcome. Sixteen (94.1%) patients with or without CCEPs who underwent network surgery had favorable prognosis except for a case with wide traumatic epilepsy. Intraoperative CCEP measurements and HGA mapping enabled visualization of pathological networks and clinical impotence as a biomarker to improve functional prognosis. HGA/CCEP recordi

Keywords: Neural signal processing, Neural Interfaces - Implantable systems, Neurological disorders
Abstract: Rhythmic entrainment in brain surface electrical measurements has been identified in many brain regions and is selectively diminished when a brain region becomes engaged for active computation. It has been shown that rhythmic entrainment is pathologically augmented in some diseases and released with medication or deep brain stimulation. We review the potential of rhythmic entrainment as an electrophysiology biomarker and illustrate how it might be used in closed loop recording and stimulation implanted devices.

Keywords: Brain-computer/machine Interface, Neural signal processing, Human performance
Abstract: We here present two novel tools for invasive brain-computer interfaces: (1) an advanced high-gamma-activity estimator via adaptive Kalman filtering and (2) an improved classification method based on linear discriminant analysis. We demonstrate our methods' superiority over conventional approaches by retrospective performance studies involving epilepsy patients with implanted subdural electrodes grids.

Keywords: Neurorehabilitation - Virtual reality, Neurorehabilitation, Neural Interfaces - Neural stimulation
Abstract: The complexity of the pathophysiology of neuropathic pain, imposes great challenges in the development of effective therapeutic approaches. This is even worse when taking into account the emotional and cognitive negative consequences of the experience of a long-term pain. Bioelectronic medicine, and in particular the stimulation of the peripheral nerves, recently has indicated several promising venues in this field. The general idea of this therapeutic approach is to cover referred painful areas through electrical stimulation that elicits more pleasant sensations. However, this can be a challenge when adopting a noninvasive approach (without implant close to nerve or spine), due to its low selectivity and reported naturalness. Here, we propose a novel multimodal noninvasive platform that aims at enhancing the naturalness of the noninvasive electrical stimulation. We combined Transcutaneous Electrical Nerve Stimulation (TENS) and Virtual Reality (VR) to allow the perceptual illusion of a more pleasant sensation thanks to a time-congruent visuo-tactile stimulation. Our preliminary results in four healthy subjects and one neuropathic patient show the feasibility of our platform in eliciting such sensations and potentially treating neuropathic symptoms.

Keywords: Neural Interfaces - Neural stimulation, Neural Interfaces - Sensors and body Interfaces
Abstract: Stimulating the vagus nerve is of great clinical interest, particularly transcutaneous auricular vagus nerve stimulation (taVNS), a noninvasive method of applying current at the cymba conchae of the outer ear to target the auricular branch of the vagus nerve. Recent efforts have shown therapeutic effects of taVNS on clinical populations, but the mechanism and autonomic nervous system (ANS) responses are not well understood. The ANS maintains physiological homeostasis through control of internal organs, including the heart, blood vessels, and pupils. Instead of invasively accessing neural targets, standard clinical measures can record ANS-dependent signals. Twenty-one individuals were tested in four sessions over two weeks; taVNS was applied at the left ear while noninvasive sensors captured electrocardiography, blood pressure, breathing, electrodermal activity (EDA), and pupil diameter (PD). From raw vitals, heart rate, mean arterial pressure (MAP), heart rate variability, EDA, and PD responses were derived and averaged across all participants. Summary statistics showed increased PD during the ramp up period of stimulation and increased variability in MAP and EDA during stimulation that persisted during recovery. These results suggest that transient PD responses could be candidate biomarkers taVNS optimization. Combining multimodal sensors and controlled autonomic testing may provide insight for objective evaluation of treatment efficacy or measurement of disease progression.

Keywords: Neural Interfaces - Neural stimulation, Neural Interfaces - Implantable systems, Neural Interfaces - Neural microsystems and Interface engineering
Abstract: In the bioelectronic medicine field, vagus nerve stimulation (VNS) is a promising technique that is expected to treat numerous inflammatory conditions, in addition to the currently FDA approved treatment for epilepsy, depression and obesity [1]. However, current VNS techniques are still limited in the spatial resolution that they can achieve, which limits its therapeutic effect and induces side effects such as coughing, headache and throat pain. In our prior work, we presented a curved ultrasound (US) transducer array with a diameter of 2 mm and with 112 miniature US transducer elements, small enough to be wrapped around the vagus nerve for precise ultrasound nerve stimulation [2]. Due to the curved alignment of the US transducers with 48 of the elements simultaneously excited, the emitted US was naturally focused at the center of the curvature. Building on this work, we employ a beam steering technique to move the focal spot to arbitrary locations within the focal plane of the transducer array. The beam steering was controlled through an in-house built US driver system and was visualized using a pulsed laser schlieren system. The propagation of the US pulse in water was imaged and recorded. This method was found to be a rapid and effective means of visualizing the US propagation.

Keywords: Neural Interfaces - Neuromorphic engineering, Brain physiology and modeling
Abstract: Chronic diseases can greatly benefit from bioelectronic medicine approaches. Neuromorphic electronic circuits present ideal characteristics for the development of brain-inspired low-power implantable processing systems that can be interfaced with biological systems. These circuits, therefore, represent a promising additional tool in the tool-set of bioelectronic medicine. In this paper, we describe the main features of neuromorphic circuits that are ideally suited for continuously monitoring the physiological parameters of the body and interact with them in real-time. We propose examples of computational primitives that can be used for real-time pattern generation and present a neuromorphic implementation of neural oscillators for the generation of sequence activation patterns. We demonstrate the features of such systems with an implementation of a three-phase network that models the dynamics of the respiratory Central Pattern Generator (CPG) and the heart chambers rhythm, and that could be used to build an adaptive pacemaker.

Keywords: Neuromuscular Systems - EMG models, processing and applications, Neurological disorders - Diagnostic and evaluation techniques
Abstract: Large, myelinated, motor A-fibers in the vagus innervate muscles of the larynx. They are implicated in side effects of cervical vagus nerve stimulation (VNS), laryngeal paralysis after surgical injury of the vagus, and in motor manifestations of progressive bulbar palsy. Knowledge of the degree of engagement of A-fibers by vagal stimuli is desirable for optimization of VNS parameters and for functional assessment of the motor neuron-laryngeal muscle projection. In mice, the most widely used animal model in preclinical studies, due to the short length of the vagus and the fast conduction velocity of A-fibers, direct measurement of stimulus-evoked A-fiber potentials is not feasible. Here, we describe a method to estimate vagal Α-fiber engagement in mice by measuring stimulus-evoked EMG (eEMG) from the thyroarytenoid-lateral cricoarytenoid laryngeal muscle complex using intramuscular wires. Recorded eEMG was typically biphasic, with 1.87-2.68 ms latency and 1.05-1.6 ms duration. Stimulus intensity threshold (T) for eEMG was 2.6-24uA (100us square pulses), lower than that for heart rate changes, which indicate engagement of B-fibers (3-7xT; 10.4-72 μA), and for breathing changes, which indicate engagement of C-fibers (9-25xT; 65-216 μA). eEMG amplitude increased with stimulus intensity and saturated at around 7-10xT (20-65 μA). Vagotomy caudal to the stimulating electrode and rostral to the recurrent laryngeal nerve resulted in disappearance of the eEMG.

Keywords: Neural Interfaces - Neural stimulation, Motor Neuroprostheses - Neuromuscular stimulation, Neuromuscular Systems - Locomotion, posture and balance
Abstract: This paper presents the development of a Dorsal Root Ganglion (DRG) stimulator system intended for use in early clinical trials for motor recovery after Spinal Cord Injury (SCI). It allows for independent control of multisite/multilevel bilateral (on both sides of the spinal cord) stimulation, it can supply a high output current of 25.4mA, and has the ability to program pulse sequences similar to actual locomotion patterns. These characteristics ultimately provide the required versatility for examining the effects of DRG stimulation on locomotion recovery, which is lacking in currently available commercial systems. The device is created using commercially available components to make the design reproducible by other research labs and to facilitate the critical approval procedure for use in a clinical research environment. Throughout the design phase, essential considerations regarding the safety of the participating patient, as well as of the medical personnel involved, were taken into account and these are analyzed and demonstrated in this paper. Such considerations are very rarely discussed in scientific literature and the authors consider that, apart from the design of the system itself, this discussion is a critical contribution of this paper.

Keywords: Neural signal processing
Abstract: Interictal epileptiform discharges (IEDs), or "spikes", are indicative of seizures and are useful in the diagnosis of epilepsy. Automated algorithms like machine learning models have shown promise in detecting spikes without requiring human supervision. However, there is still a lack of comprehensive, high-quality, and accessible data that can be used to train these algorithms. The goal of this work is to assess whether Generative Adversarial Networks (GANs) can create synthetic spikes that are both realistic and that can improve the performance of machine learning classification. Here we developed an Auxiliary Classifier GAN (AC-GAN) to generate synthetic spikes and trained it on expert-annotated real spike events from intracranial EEG recordings. We found that the AC-GAN was capable of successfully generating realistic synthetic spikes, demonstrated by qualitative and quantitative metrics. Furthermore, the synthetic spikes improved classification accuracy when augmented to the training data. Our findings exemplify the utility of GANs in assisting with epilepsy diagnosis through synthesis of realistic neurophysiological signal waveforms and through data augmentation in classification.

Keywords: Neural signal processing, Brain physiology and modeling
Abstract: The longstanding theory of “parallel processing” predicts that, except for a sign reversal, ON and OFF cells are driven by a similar pre-synaptic circuit and have similar visual field coverage, direction/orientation selectivity, visual acuity and other functional properties. However, recent experimental data challenges this view. Here we present an information theory based receptive field (RF) estimation method - quadratic mutual information (QMI) - applied to multi-electrode array electrophysiological recordings from the mouse dorsal lateral geniculate nucleus (dLGN). This estimation method provides more accurate RF estimates than the commonly used Spike-Triggered Average (STA) method, particularly in the presence of spatially correlated inputs. This improved efficiency allowed a larger number of RFs (285 vs 189 cells) to be extracted from a previously published dataset. Fitting a spatial-temporal Difference-of-Gaussians (ST-DoG) model to the RFs revealed that while the structural RF properties of ON and OFF cells are largely symmetric, there were some asymmetries apparent in the functional properties of ON and OFF visual processing streams - with OFF cells preferring higher spatial and temporal frequencies on average, and showing a greater degree of orientation selectivity.

Keywords: Brain-computer/machine Interface, Neural Signal Processing - Blind source separation, Neural Interfaces - Recording
Abstract: Artifact removal is a key step toward designing real-world and efficient brain computer interfaces. Here we describe an automatic blind source separation algorithm applicable to real-time signal processing. The algorithm combines the generalized eigenvalue decomposition technique with Laplacian filtering to separate desired and undesired subspaces, exclude artifact sources and recover artifact-free cortical signals. The algorithm outperforms commonly used artifact removal methods in brain computer interfaces as measured by cortical decoding performance.

Keywords: Neural signal processing, Brain Functional Imaging - EEG and Evoked Potentials, Neural Interfaces - Recording
Abstract: The accelerating trend towards personalized ``precision medicine'' and tele-healthcare is revolutionizing the practice of medicine and giving the individual unprecedented access to their own health data. At the same time, a widening gap between wakeful health (ex. physical activity) and nocturnal health (sleep) has revealed the need for accurate, reliable and automated methods to measure sleep in the home. Here we describe a small-footprint sensor array, using electrode stickers that can be self-applied to the forehead, in conjunction with an automated scoring algorithm that achieves accuracies on par with trained human experts (77% agreement using a five-class taxonomy). Compared to alternatives, this approach avoids the low signal-to-noise ratios of dry-contact scalp electrodes while also circumventing the need to measure through hair. Critically, it does not require a trained human expert, either to apply the electrodes or to translate the signals into a useful description of sleep patterns. Taken together, this represents an exciting step forward towards affordable, reliable, and accurate in-the-home sleep assessment.

Keywords: Motor Neuroprostheses - Prostheses, Neuromuscular Systems - EMG models, processing and applications, Neuromuscular Systems - Wearable systems
Abstract: Myoelectric controllers for upper limb prostheses are susceptible to signal disturbances across practical conditions. In particular, electrode liftoff or wire breakage introduce interface noise that, even if only present in a single channel, is detrimental to controller performance. We trained a supervised denoising variational autoencoder to learn a low-dimensional subspace underlying muscle activation patterns that was robust to noise in single EMG channels. Two latent space classifiers, which used the deep learning model, and two conventional LDA-based classifiers were used to classify wrist and hand gestures from clean and synthetically corrupted EMG signals. The baseline LDA classifier, trained on clean data only, suffered a marked increase in errors when evaluated on the corrupted data. The second LDA classifier, trained on clean and corrupted data, improved robustness to noise. Regardless, both latent space methods significantly outperformed both LDA methods in classifying clean and corrupted data. These results highlight that interface noise has adverse effects on current pattern recognition controllers but that deep learning inspired latent space classifiers can mitigate these effects and achieve highly accurate movement classification.

Keywords: Motor learning, neural control, and neuromuscular systems, Neuromuscular Systems - Locomotion, posture and balance, Brain-computer/machine Interface
Abstract: Most sensorimotor studies investigating the covariation of populations of neurons in primary motor cortex (M1) have considered only a few trained movements made under highly constrained conditions. However, motor behaviors in daily living happen in a far more complex and varied contexts. It is unclear whether M1 neurons would have different population responses in a more naturalistic, unconstrained setting, including requirements to accommodate multiple limbs and body posture, and more extensive proprioceptive inputs. Here, we recorded M1 spiking signals while a monkey performed hand grasp movements in two different contexts: one in the typical constrained lab setting, and the other while moving freely in a large plastic cage. We compared the covariance patterns of the neural activity during movements across the two contexts. We found that the neural covariation patterns accompanying two different hand grasps in the unconstrained context were largely preserved, while they differed across contexts, even for the same type of grasp. We also found that the M1 population activity was confined to context-dependent neural manifolds, but these manifolds were not completely independent, as some dimensions appeared to be shared across the contexts. These results suggest that the coordinated activity of M1 neurons is strongly dependent on behavioral context, in ways that were not entirely anticipated.

Keywords: Neural Interfaces - Computational modeling and simulation
Abstract: Ultrasound Stimulation (US) is emerging as a promising technology for noninvasive brain stimulation, but its applicability to peripheral circuits remains unclear. To better understand the potential of US for peripheral neuromodulation, we combined computational and experimental investigations. First, we designed a computational framework enabling the simulation of a candidate mechanism of US neuromodulation (intramembrane cavitation) in morphologically realistic, multi-compartment models of peripheral nerve fibers. This framework revealed the existence of distinct US subspaces to achieve the selective recruitment of myelinated and/or unmyelinated fibers, suggesting an advantageous functional selectivity US over electrical stimulation. Second, to assess the validity of these predictions and their translatability to realistic environments, we recorded US-evoked responses in both isolated nerve bundles and exposed sciatic nerves of rats. These investigations pave the way to a systematic characterization of US peripheral neuromodulation.

Keywords: Brain Stimulation - Transcranial Ultrasound Stimulation (TUS)
Abstract: Neuromodulation can be achieved either with noninvasive techniques that are depth limited or invasive procedures that can go to large depths. Over the past few years, transcranial focused ultrasound (FUS) has been shown capable of both stimulating and suppressing brain activity in vivo. Ultrasound has several advantages over the aforementioned technologies for deep brain stimulation as it can penetrate the brain over several centimeters through the intact scalp and skull. Given its entirely noninvasive and nonionizing nature, the technique has been shown to be translatable to human brain studies with deep penetration (of several centimeters) without requiring introduction of electrodes or optic fibers inside the brain. Our group has been studying the noninvasive stimulation and inhibition of the central and peripheral nervous systems in live animals. In the brain, we have shown that focused ultrasound is capable of noninvasively eliciting motor and sensory responses when distinct brain regions are targeted. In the periphery, when the ultrasound beam is focused on the sciatic nerve in vivo, the thigh muscle is activated and muscle twitches can be induced at low ultrasonic intensities while the same twitches can be inhibited at higher intensities due to associated temperature rise that inhibits nerve firing. Cellular and fiber responses in excised tissue have confirmed the live animal responses. An overview of the aforementioned findings will be presented.

Keywords: Neural Interfaces - Microelectrode and fabrication technologies, Neural Interfaces - Implantable systems, Neural Interfaces - Sensors and body Interfaces
Abstract: Neural interfacing of small and fragile peripheral nerves, particularly those in the autonomic nervous system, present unique challenges that demand innovation in neural interfaces including new flexible and sensitive materials, electrode geometrical configurations, and interpretation of the complex electrophysiological signals. Many of these nerves are composed by multiple fascicles, ranging between 50-500 um in diameter, and formed mainly by unmyelinated axons. Some travel between blood vessels or surrounded by fatty tissue, which complicates their isolation and electrode placing. In addition, the information carried by these nerves is highly heterogeneous as their axons provide information from and to multiple organs, or integrate a number of functions that change dynamically with specific physiological conditions. This mimi-symposium will present recent progress in new microelectrodes designed to interrogate intraneurally the activity of these nerves. In addition, new methods to interface the neural activity from the viceral neuro-vascular bundle and stimulation parameters for activating this plexi will be covered. This mini-symposium will highlight the challenges associated with effective and chronic interfacing of these small nerves, and provide an insight to the developing new neural interfacing tools to understand and modulate, the neural circuits in the autonomic nervous system.

Keywords: Neural Interfaces - Microelectrode and fabrication technologies, Neural Interfaces - Recording, Neural Interfaces - Neural microsystems and Interface engineering
Abstract: Vagus nerve stimulation leads to the decrease of inflammatory cytokines released by the spleen, which has been proposed as neuromodulation treatment of rheumatoid arthritis and bowel disease. However, deleterious side effects due to off-target activation limit this application. Near-organ neuromodulation offers a more selective approach, but represent an anatomical challenge for neural interfacing, since the splenic nerve fascicles travel along blood vessels forming a neuro-vascular plexi (NVP). To address this limitation, we developed a mechanical and electrochemical robust platinized fibers with reduced graphene oxide (rGO-Pt, aka sutrodes), to simultaneously interface the four NVP branches. The selectivity and sensitivity of this method was demonstrated in neural recording from separate terminal NVP branches. Our data provide insight into the functional compartmentalization of splenic neurophysiology, and selective terminal organ neuromodulation for inflammatory diseases.

Keywords: Neural Interfaces - Recording, Neural Interfaces - Microelectrode and fabrication technologies
Abstract: Intraneural recordings from autonomic nerves are useful for understanding neural control over organ function. Microelectrodes with a minimal impact on these small nerves are needed. We are developing carbon fiber microelectrode arrays. We recorded action potentials from rat vagus nerve and feline dorsal root ganglia under anesthesia. Next we are working on a carbon fiber array for use in long-term recording studies.

Keywords: Neural Interfaces - Neural stimulation, Neural Interfaces - Sensors and body Interfaces
Abstract: Cervical vagus nerve stimulation (VNS) is tested as potential treatment for several disorders of the brain and peripheral organs. With this work, we describe a systematic framework of stimulus manipulation and parameter optimization to target activation of large-, intermediate- or small-size vagal fibers in individual subjects, both rats and mice. Fiber-selective VNS therapy is clinically important, as it allows providing precise autonomic neuromodulation with better efficacy and minimum off-target effects.

Keywords: Neural Interfaces - Neural stimulation, Neural Interfaces - Implantable systems, Neural Interfaces - Computational modeling and simulation
Abstract: Neuromodulation of the immune system is a novel therapeutic strategy for the treatment of inflammatory conditions. Activation of the vagus nerve has been shown to modulate inflammation in animal models, and early feasibility trials in inflammatory diseases have provided initial indication of the same in the clinic. Targeting nerves directly innervating the spleen may achieve more complete nerve activation at the organ, an improved side effect profile, and consequently a greater therapeutic window. However, targeting this sub-division of the nervous system presents specific challenges in translation to the clinic. Firstly, autonomic nerves of the viscera are typically embedded non-uniformly among disparate tissues with complex interfacing requirements. Secondly, these nerves contain axons with complex activation properties. Thirdly, traditional proof of concept in vivo models have limited translational relevance regarding stimulation parameters and device design. Here we demonstrate approaches for the development of neuromodulation requirements for the nerves directly innervating the spleen to restore inflammatory homeostasis. Through the use of in silico modelling of target anatomy, and the validation of these estimates through ex vivo tissue electrophysiology studies, coupled with the development of translationally relevant large animal models for safety and target engagement proof of concept, we present a R&D process for a neural interface addressing a novel nerve target

Keywords: Brain physiology and modeling - Neural dynamics and computation, Brain-computer/machine Interface, Motor neuroprostheses
Abstract: Modeling how neural population dynamics explain a specific behavior is a key challenge. Neural activity can simultaneously relate to multiple behaviors and internal states. This necessitates dissociating those neural dynamics that are related to the specific behavior of interest from other dynamics. We recently developed a new modeling algorithm termed preferential subspace identification (PSID) that can dissociate and prioritize behaviorally relevant neural dynamics by considering both neural activity and behavior when learning a model. We showed in multiple non-human primate neural datasets that PSID learns more accurate models of behaviorally relevant neural dynamics and reveals a much lower dimensionality for these dynamics. Moreover, PSID discovers distinct rotational neural dynamics during 3D arm movements that are more predictive of behavior and not found by standard neural dynamic modeling methods. Finally, PSID achieves more accurate decoding of behavior. Our results show the utility of PSID for dimensionality reduction and for modeling of neural population activity while dissociating and preserving its behaviorally relevant neural dynamics.

Keywords: Brain physiology and modeling - Neural dynamics and computation, Brain Physiology and Modeling - Neural circuits, Brain physiology and modeling
Abstract: Statistical regularities in the environment create prior beliefs that we rely on to optimize our behavior when sensory information is uncertain. Bayesian theory formalizes how prior beliefs can be leveraged and has had a major impact on models of perception, sensorimotor function, and cognition. However, it is not known how recurrent interactions among neurons mediate Bayesian integration. Using a time interval reproduction task in monkeys, we found that prior statistics warp the underlying structure of population activity in the frontal cortex, allowing the mapping of sensory inputs to motor outputs to be biased in accordance with Bayesian inference. Analysis of neural network models performing the task revealed that this warping was implemented through a low-dimensional curved manifold, which allowed us to further probe the potential causal underpinnings of this computational strategy. These results uncover a simple and general principle whereby prior beliefs exert their influence on behavior by sculpting latent cortical dynamics.

Keywords: Brain-computer/machine Interface, Motor neuroprostheses, Motor Neuroprostheses - Prostheses
Abstract: A growing body of research suggests that cortical neural ensemble activity during motor behaviors is well-described by orderly dynamics in a low-dimensional underlying neural state space. Most of this prior work has examined neural activity during limb movements, in nonhuman primates (NHPs). We report here that there is similar neural population structure during different types of movements, including during the uniquely human behavior of speaking. Learning this neural population structure has helped improve decoders for hand-movement brain-computer interfaces (BCIs) in NHPs, and we suggest here that learning this structure may also help improve BCI decoders for synthesizing speech in people.

Keywords: Brain Stimulation - Optogenetics, Neural Interfaces - Neural stimulation, Neural Interfaces - Recording
Abstract: The rapid acceleration of tools for recording neuronal populations and targeted optogenetic manipulation has enabled real-time, feedback control of neuronal circuits in the brain. Continuously-graded control of measured neuronal activity poses a wide range of technical challenges, which we address through a combination of optogenetic stimulation and a state-space optimal control framework implemented in the thalamocortical circuit of the awake mouse. Closed-loop optogenetic control of neurons was performed in real-time via stimulation of channelrhodopsin-2 expressed in the somatosensory thalamus of the head-fixed mouse. A state-space linear dynamical system model structure was used to approximate the light-to-spiking input-output relationship in both single-neuron as well as multi-neuron scenarios when recording from multielectrode arrays. Feedback control of neuronal circuits opens the door to adaptively interacting with the dynamics underlying sensory, motor, and cognitive signaling, enabling a deeper understanding of circuit function and ultimately the control of function in the face of injury or disease.

Keywords: Brain-computer/machine Interface, Motor learning, neural control, and neuromuscular systems
Abstract: Internal states such as arousal, attention, and motivation are known to modulate brain-wide neural activity, but how these processes interact with learning is not well understood. During learning, the brain must modify the neural activity it produces to improve behavioral performance. How do internal states affect the evolution of this learning process? Using a brain-computer interface (BCI) learning paradigm in non-human primates, we identified large fluctuations in neural population activity in motor cortex (M1) indicative of arousal-like internal state changes. These fluctuations drove population activity along dimensions we term neural engagement axes. Neural engagement increased abruptly at the start of learning, and then gradually retreated. In a BCI, the causal relationship between neural activity and behavior is known. This allowed us to understand how these changes impacted behavioral performance for different task goals. We found that neural engagement interacted with learning, helping to explain why animals learned some task goals more quickly than others.

Keywords: Motor learning, neural control, and neuromuscular systems, Brain physiology and modeling - Neural dynamics and computation
Abstract: Learned movements can be skillfully performed at different paces. What neural strategies produce this flexibility? Can they be predicted and understood by network modeling? We trained monkeys to perform a cycling task at different speeds, and trained artificial recurrent networks to generate the empirical muscle-activity patterns. Network solutions reflected the principle that smooth well-behaved dynamics require low trajectory tangling, and yielded quantitative and qualitative predictions. To evaluate predictions, we recorded motor cortex population activity during the same task. Responses supported the hypothesis that the dominant neural signals reflect not muscle activity, but network-level strategies for generating muscle activity. Single-neuron responses were better accounted for by network activity than by muscle activity. Similarly, neural population trajectories shared their organization not with muscle trajectories, but with network solutions. Thus, cortical activity could be understood based on the need to generate muscle activity via dynamics that allow smooth, robust control over movement speed.