Phase-Approaching Stimulation Sequence for SSVEP-Based BCI: A Practical Use in VR/AR HMD
In this study, a six-command SSVEP-based BCI was designed to operate a flying drone. The ITR and detection accuracy are 36.84 bits/min and 93.30%, respectively. (Source: IEE Transactions on Neural Systems and Rehabilitation Engineering)
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 14, 2022 Category: Neuroscience Source Type: research

Deep Correlation Analysis for Audio-EEG Decoding
The electroencephalography (EEG), which is one of the easiest modes of recording brain activations in a non-invasive manner, is often distorted due to recording artifacts which adversely impacts the stimulus-response analysis. The most prominent techniques thus far attempt to improve the stimulus-response correlations using linear methods. In this paper, we propose a neural network based correlation analysis framework that significantly improves over the linear methods for auditory stimuli. A deep model is proposed for intra-subject audio-EEG analysis based on directly optimizing the correlation loss. Further, a neural net...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 14, 2022 Category: Neuroscience Source Type: research

Real-Time Optimization of Retinal Ganglion Cell Spatial Activity in Response to Epiretinal Stimulation
In this study we use calcium imaging, in vitro retina, neural networks (NN), and an optimization algorithm to demonstrate a method to iteratively search for optimal stimulation parameters that create focal RGC activation. Our findings indicate that we can converge to stimulation parameters that result in focal RGC activation by sampling less than 1/3 of the parameter space. A similar process implemented clinically can reduce time required for optimizing implant operation and enable personalized fitting of retinal prostheses. (Source: IEE Transactions on Neural Systems and Rehabilitation Engineering)
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 9, 2022 Category: Neuroscience Source Type: research

Neural Decoding of Chinese Sign Language With Machine Learning for Brain–Computer Interfaces
Limb motion decoding is an important part of brain-computer interface (BCI) research. Among the limb motion, sign language not only contains rich semantic information and abundant maneuverable actions but also provides different executable commands. However, many researchers focus on decoding the gross motor skills, such as the decoding of ordinary motor imagery or simple upper limb movements. Here we explored the neural features and decoding of Chinese sign language from electroencephalograph (EEG) signal with motor imagery and motor execution. Sign language not only contains rich semantic information, but also has abunda...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 9, 2022 Category: Neuroscience Source Type: research

Wearable Lower-Limb Exoskeleton for Children With Cerebral Palsy: A Systematic Review of Mechanical Design, Actuation Type, Control Strategy, and Clinical Evaluation
Children with a neurological disorder such as cerebral palsy (CP) severely suffer from a reduced quality of life because of decreasing independence and mobility. Although there is no cure yet, a lower-limb exoskeleton (LLE) has considerable potential to help these children experience better mobility during overground walking. The research in wearable exoskeletons for children with CP is still at an early stage. This paper shows that the number of published papers on LLEs assisting children with CP has significantly increased in recent years; however, no research has been carried out to review these studies systematically. ...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 9, 2022 Category: Neuroscience Source Type: research

A Layer Jamming Soft Glove for Hand Tremor Suppression
Tremors are a common movement disorder that affects a person’s life adversely. With various drawbacks of current treatment methods, there is a need for a mechanical solution. The authors present a soft orthosis based on layer jamming for the suppression of hand tremors. A vacuum supplied to the layer jamming elements which contain a stack of layers attached to the glove leads to increased stiffness in the glove, suppressing the tremor. The behavior of the tremor in a cohort of patients in Sri Lanka was studied and showed that the tremor’s mean frequency was 5.05 ± 2.03 Hz. An existing analytical model wa...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 9, 2022 Category: Neuroscience Source Type: research

An Objective, Information-Based Approach for Selecting the Number of Muscle Synergies to be Extracted via Non-Negative Matrix Factorization
Muscle synergy analysis is a useful tool for the evaluation of the motor control strategies and for the quantification of motor performance. Among the parameters that can be extracted, most of the information is included in the rank of the modular control model (i.e. the number of muscle synergies that can be used to describe the overall muscle coordination). Even though different criteria have been proposed in literature, an objective criterion for the model order selection is needed to improve reliability and repeatability of MSA results. In this paper, we propose an Akaike Information Criterion (AIC)-based method for mo...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 9, 2022 Category: Neuroscience Source Type: research

Accuracy of Temporo-Spatial and Lower Limb Joint Kinematics Parameters Using OpenPose for Various Gait Patterns With Orthosis
This study investigates the capability of a single camera-based pose estimation system using OpenPose (OP) to measure the temporo-spatial and joint kinematics parameters during gait with orthosis. Eleven healthy adult males walked under different conditions of speed and foot progression angle (FPA). Temporo-spatial and joint kinematics parameters were measured using a single camera-based system with OP and a three-dimensional motion capture system. The limit of agreement, mean absolute error, absolute agreement (ICC2, 1), and relative consistency (ICC3, 1) between the systems under each condition were assessed for reliabil...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 28, 2021 Category: Neuroscience Source Type: research

Model-Based Step Length Estimation Using a Pendant-Integrated Mobility Sensor
We present a model-based approach to estimate the step length, which is divided into two successive steps. As the first part of our approach, we present an algorithm for estimation of the vertical displacement of the center of mass (CoM) during gait. Based on this estimate, we present a novel approach to estimate the step length, which we have deduced from a previously published, simplified gait model. The algorithm is compared to a commonly known approach for accelometry-based step length prediction and validated against reference data obtained from a force plate-integrated treadmill for gait analysis during a clinical st...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 28, 2021 Category: Neuroscience Source Type: research

Applying Hip Stiffness With an Exoskeleton to Compensate Gait Kinematics
In this study, applying virtual stiffness using a hip exoskeleton was investigated as a possible method to guide users to change their gait kinematics. With a view to applications in locomotor rehabilitation, either to provide assistance or promote recovery, this study assessed whether imposed stiffness induced changes in the gait pattern during walking; and whether any changes persisted upon removal of the intervention, which would indicate changes in central neuro-motor control. Both positive and negative stiffness induced immediate and persistent changes of gait kinematics. However, the results showed little behavioral ...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 28, 2021 Category: Neuroscience Source Type: research

Performance of Sonomyographic and Electromyographic Sensing for Continuous Estimation of Joint Torque During Ambulation on Multiple Terrains
Advances in powered assistive device technology, including the ability to provide net mechanical power to multiple joints within a single device, have the potential to dramatically improve the mobility and restore independence to their users. However, these devices rely on the ability of their users to continuously control multiple powered lower-limb joints simultaneously. Success of such approaches rely on robust sensing of user intent and accurate mapping to device control parameters. Here, we compare two non-invasive sensing modalities: surface electromyography and sonomyography, (i.e., ultrasound imaging of skeletal mu...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 24, 2021 Category: Neuroscience Source Type: research

Toward Real-Time Muscle Force Inference and Device Control via Optical-Flow-Tracked Muscle Deformation
Despite the utility of musculoskeletal dynamics modeling, there exists no safe, noninvasive method of measuring in vivo muscle output force in real time — limiting both biomechanical insight into dexterous motion and intuitive control of assistive devices. In this paper, we demonstrate that muscle deformation constitutes a promising, yet unexplored signal from which to 1) infer such forces and 2) build novel device control schemes. Through a case study of the elbow joint on a preliminary cohort of 10 subjects, we show that muscle deformation (specifically, thickness change of the brachioradialis, as measured via ultr...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 24, 2021 Category: Neuroscience Source Type: research

Filter Bank Convolutional Neural Network for Short Time-Window Steady-State Visual Evoked Potential Classification
Convolutional neural network (CNN) has been gradually applied to steady-state visual evoked potential (SSVEP) of the brain-computer interface (BCI). Frequency-domain features extracted by fast Fourier Transform (FFT) or time-domain signals are used as network input. In the frequency-domain diagram, the features at the short time-window are not obvious and the phase information of each electrode channel may be ignored as well. Hence we propose a time-domain-based CNN method (tCNN), using the time-domain signal as network input. And the filter bank tCNN (FB-tCNN) is further proposed to improve its performance in the short ti...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 24, 2021 Category: Neuroscience Source Type: research

A Novel Online Action Observation-Based Brain–Computer Interface That Enhances Event-Related Desynchronization
Brain-computer interface (BCI)-based stroke rehabilitation is an emerging field in which different studies have reported variable outcomes. Among the BCI paradigms, motor imagery (MI)-based closed-loop BCI is still the main pattern in rehabilitation training. It can estimate a patient’ motor intention and provide corresponding feedback. However, the individual difference in the ability to generate event-related desynchronization (ERD) and the low classification accuracy of the multi-class scenario restrict the application of MI-based BCI. In the current study, a novel online action observation (AO)–based BCI wa...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 24, 2021 Category: Neuroscience Source Type: research

A Probability Distribution Model-Based Approach for Foot Placement Prediction in the Early Swing Phase With a Wearable IMU Sensor
This study is also expected to inspire additional probabilistic gait analysis works. (Source: IEE Transactions on Neural Systems and Rehabilitation Engineering)
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 24, 2021 Category: Neuroscience Source Type: research

A Reliable Fall Detection System Based on Analyzing the Physical Activities of Older Adults Living in Long-Term Care Facilities
Fall detection systems are designed in view to reduce the serious consequences of falls thanks to the early automatic detection that enables a timely medical intervention. The majority of the state-of-the-art fall detection systems are based on machine learning (ML). For training and performance evaluation, they use some datasets that are collected following predefined simulation protocols i.e. subjects are asked to perform different types of activities and to repeat them several times. Apart from the quality of simulating the activities, protocol-based data collection results in big differences between the distribution of...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 24, 2021 Category: Neuroscience Source Type: research

Computational Study on Spatially Distributed Sequential Stimulation for Fatigue Resistant Neuromuscular Electrical Stimulation
Neuromuscular electrical stimulation (NMES) is used to artificially induce muscle contractions of paralyzed limbs in individuals with stroke or spinal cord injury, however, the therapeutic efficacy can be significantly limited by rapid fatiguing of the targeted muscle. A unique stimulation method, called spatially distributed sequential stimulation (SDSS), has been shown clinically to reduce fatiguing during FES, but further improvement is needed. The purpose of this study was to gain a better understanding of SDSS-induced neural activation in the human lower leg using a computational approach. We developed a realistic fin...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 24, 2021 Category: Neuroscience Source Type: research

Effect of BCI-Controlled Pedaling Training System With Multiple Modalities of Feedback on Motor and Cognitive Function Rehabilitation of Early Subacute Stroke Patients
This study was a randomized placebo-controlled blinded-endpoint clinical trial to investigate the effects of a BCI-controlled pedaling training system (BCI-PT) on the motor and cognitive function of stroke patients during rehabilitation. A total of 30 early subacute ischemic stroke patients with hemiplegia and cognitive impairment were randomly assigned to the BCI-PT or traditional pedaling training. We used single-channel Fp1 to collect electroencephalography data and analyze the attention index. The BCI-PT system timely provided visual, auditory, and somatosensory feedback to enhance the patient’s participation to ...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 24, 2021 Category: Neuroscience Source Type: research

Quantification of Upper Limb Isometric Force Control Abilities for Evaluating Upper Limb Functions Among Prosthetic Users
This study aimed to quantify upper limb isometric force control abilities in healthy individuals and prosthetic users using a custom-built handle with a 6-axis force/torque sensor and visual cue, namely an Upper Limb End-effector type Force control test device (ULEF). Feasibilities of the test device were demonstrated through experiments by holding the ULEF with an intact hand among healthy subjects and transradial and wrist amputees with a myoelectric powered prosthetic hand, the bebionic hand. Compared to the healthy individuals, the prosthetic user group demonstrated poor isometric force control abilities in terms of hi...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 21, 2021 Category: Neuroscience Source Type: research

A Comparison Between Conventional and Terrain-Specific Adaptive Pushrim-Activated Power-Assisted Wheelchairs
In this study, we aimed to develop an adaptive PAPAW controller that responds effectively to changes in environmental conditions (e.g., type of surface or terrain). Experiments were conducted to collect kinematics of wheelchair motion using a frame-mounted inertial measurement unit (IMU) while performing a variety of wheelchair activities on different indoor/outdoor terrains. Statistical characteristics of velocity and acceleration measurements were extracted and used to develop a terrain classification framework to identify certain indoor and outdoor terrains. The terrain classification framework, based on random forest c...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 17, 2021 Category: Neuroscience Source Type: research

Low Frequency Transcranial Alternating Current Stimulation Accelerates Sleep Onset Process
Conclusion: The intervention of low frequency brain rhythmic transcranial alternating current stimulation may induce accelerated effect on sleep onset process, thereby possibly alleviating the problems related to sleep disorders such as difficulty to reach the real sleep state quickly after lying down. (Source: IEE Transactions on Neural Systems and Rehabilitation Engineering)
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 17, 2021 Category: Neuroscience Source Type: research

A-GAS: A Probabilistic Approach for Generating Automated Gait Assessment Score for Cerebral Palsy Children
Gait disorders in children with cerebral palsy (CP) affect their mental, physical, economic, and social lives. Gait assessment is one of the essential steps of gait management. It has been widely used for clinical decision making and evaluation of different treatment outcomes. However, most of the present methods of gait assessment are subjective, less sensitive to small pathological changes, time-taking and need a great effort of an expert. This work proposes an automated, comprehensive gait assessment score (A-GAS) for gait disorders in CP. Kinematic data of 356 CP and 41 typically developing subjects is used to validate...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 17, 2021 Category: Neuroscience Source Type: research

Whole Body Center of Mass Feedback in a Reflex-Based Neuromuscular Model Predicts Ankle Strategy During Perturbed Walking
Active prosthetic and orthotic devices have the potential to increase quality of life for individuals with impaired mobility. However, more research into human-like control methods is needed to create seamless interaction between device and user. In forward simulations the reflex-based neuromuscular model (RNM) by Song and Geyer shows promising similarities with real human gait in unperturbed conditions. The goal of this work was to validate and, if needed, extend the RNM to reproduce human kinematics and kinetics during walking in unperturbed and perturbed conditions. The RNM was optimized to reproduce joint torque, calcu...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 17, 2021 Category: Neuroscience Source Type: research

A Comparison Between Conventional and User-Intention-Based Adaptive Pushrim-Activated Power-Assisted Wheelchairs
Pushrim-activated power-assisted wheel (PAPAW) users ideally require different levels of assistance depending on activity and preference. Therefore, it is important to design and develop adaptive PAPAW controllers to account for these differences. The main objective of this work was to integrate a user intention estimation framework into a PAPAW and develop personalized adaptive controllers. We performed experiments to gather kinetics of wheelchair propulsion for a variety of daily life wheelchair activities. The propulsion characteristics (i.e., pushrim forces) were used to train intention estimation models and characteri...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 17, 2021 Category: Neuroscience Source Type: research

A Robust Bimodal Index Reflecting Relative Dynamics of EEG and HRV With Application in Monitoring Depth of Anesthesia
Supplemental information captured from HRV can provide deeper insight into nervous system function and consequently improve evaluation of brain function. Therefore, it is of interest to combine both EEG and HRV. However, irregular nature of time spans between adjacent heartbeats makes the HRV hard to be directly fused with EEG timeseries. Current study performed a pioneering work in integrating EEG-HRV information in a single marker called cumulant ratio, quantifying how far EEG dynamics deviate from self-similarity compared to HRV dynamics. Experimental data recorded using BrainStatus device with single ECG and 10 EEG cha...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 17, 2021 Category: Neuroscience Source Type: research

Automatic Identification of Axon Bundle Activation for Epiretinal Prosthesis
Conclusion: This works presents a simple, accurate and efficient algorithm to detect axon bundle activation in epiretinal prostheses. Significance: The algorithm could be used in a closed-loop manner by a future epiretinal prosthesis to reduce poorly controlled visual percepts associated with bundle activation. Activation of distant cells via axonal stimulation will likely occur in other types of retinal implants and cortical implants, and the method may therefore be broadly applicable. (Source: IEE Transactions on Neural Systems and Rehabilitation Engineering)
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 17, 2021 Category: Neuroscience Source Type: research

Decoding Muscle Force From Motor Unit Firings Using Encoder-Decoder Networks
Appropriate interpretation of motor unit (MU) activities after surface EMG (sEMG) decomposition is a key factor to decode motor intentions in a noninvasive and physiologically meaningful way. However, there are great challenges due to the difficulty in cross-trial MU tracking and unavoidable loss of partial MU information resulting from incomplete decomposition. In light of these issues, this study presents a novel framework for interpreting MU activities and applies it to decode muscle force. The resulting MUs were clustered and classified into different categories by characterizing their spatially distributed firing wave...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 17, 2021 Category: Neuroscience Source Type: research

ScoreNet: A Neural Network-Based Post-Processing Model for Identifying Epileptic Seizure Onset and Offset in EEGs
We design an algorithm to automatically detect epileptic seizure onsets and offsets from scalp electroencephalograms (EEGs). The proposed scheme consists of two sequential steps: detecting seizure episodes from long EEG recordings, and determining seizure onsets and offsets of the detected episodes. We introduce a neural network-based model called ScoreNet to carry out the second step by better predicting the seizure probability of pre-detected seizure epochs to determine seizure onsets and offsets. A cost function called log-dice loss with a similar meaning to the F1 score is proposed to handle the natural data imbalance ...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 10, 2021 Category: Neuroscience Source Type: research

Exploring Covert States of Brain Dynamics via Fuzzy Inference Encoding
Human brain inherently exhibits latent mental processes which are likely to change rapidly over time. A framework that adopts a fuzzy inference system is proposed to model the dynamics of the human brain. The fuzzy inference system is used to encode real-world data to represent the salient features of the EEG signals. Then, an unsupervised clustering is conducted on the extracted feature space to identify the brain (external and covert) states that respond to different cognitive demands. To understand the human state change, a state transition diagram is introduced, allowing visualization of connectivity patterns between e...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 10, 2021 Category: Neuroscience Source Type: research

Low-Frequency Entrainment to Visual Motion Underlies Sign Language Comprehension
When people listen to speech, neural activity tracks the entropy fluctuation in the acoustic envelope of the signal. This signal-based entrainment has been shown to be the basis of speech parsing and comprehension. In this electroencephalography (EEG) study, we compute sign language users’ cortical tracking of changes in visual dynamics of the communicative signal in the time-direct videos of sign language, and their time-reversed counterparts, and assess the relative contribution of response frequencies between.2 and 12.4 Hz to comprehension using a machine learning approach to brain state classification. Lower freq...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 7, 2021 Category: Neuroscience Source Type: research

Spiking Characteristics of Network-Mediated Responses Arising in Direction-Selective Ganglion Cells of Rabbit and Mouse Retinas to Electric Stimulation for Retinal Prostheses
In this study, we characterized the electrically-evoked network-mediated responses (hereafter referred to as electric responses) of ON-OFF direction-selective (DS) RGCs in rabbit and mouse retinas for the first time. Interestingly, both species in common demonstrated strong negative correlations between spike counts of electric responses and direction selective indices (DSIs), suggesting electric stimulation activates inhibitory presynaptic neurons that suppress null direction responses for high direction tuning in their light responses. The DS cells of the two species showed several differences including different numbers...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - November 26, 2021 Category: Neuroscience Source Type: research

Integrated Gait Triggered Mixed Reality and Neurophysiological Monitoring as a Framework for Next-Generation Ambulatory Stroke Rehabilitation
Brain stroke affects millions of people in the world every year, with 50 to 60 percent of stroke survivors suffering from functional disabilities, for which early and sustained post-stroke rehabilitation is highly recommended. However, approximately one third of stroke patients do not receive early in hospital rehabilitation programs due to insufficient medical facilities or lack of motivation. Gait triggered mixed reality (GTMR) is a cognitive-motor dual task with multisensory feedback tailored for lower-limb post-stroke rehabilitation, which we propose as a potential method for addressing these rehabilitation challenges....
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - November 26, 2021 Category: Neuroscience Source Type: research

Histogram of States Based Assistive System for Speech Impairment Due to Neurological Disorders
Assistive speech technology is a challenging task because of the impaired nature of dysarthric speech, such as breathy voice, strained speech, distorted vowels, and consonants. Learning compact and discriminative embeddings for dysarthric speech utterances is essential for impaired speech recognition. We propose a Histogram of States (HoS)-based approach that uses Deep Neural Network-Hidden Markov Model (DNN-HMM) to learn word lattice-based compact and discriminative embeddings. Best state sequence chosen from word lattice is used to represent dysarthric speech utterance. A discriminative model-based classifier is then use...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - November 26, 2021 Category: Neuroscience Source Type: research

Classification of Left-Versus Right-Hand Motor Imagery in Stroke Patients Using Supplementary Data Generated by CycleGAN
This study proposes a surrogate EEG data-generation system based on cycle-consistent adversarial networks (CycleGAN) that can expand the number of training data. This study used EEG2Image based on a modified S-transform (MST) to convert EEG data into EEG-topography. This method retains the frequency-domain characteristics and spatial information of the EEG signals. Then, the CycleGAN is used to learn and generate motor-imagery EEG data of stroke patients. From the visual inspection, there is no difference between the EEG topographies of the generated and original EEG data collected from the stroke patients. Finally, we use...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - November 26, 2021 Category: Neuroscience Source Type: research

Combination and Comparison of Sound Coding Strategies Using Cochlear Implant Simulation With Mandarin Speech
Three cochlear implant (CI) sound coding strategies were combined in the same signal processing path and compared for speech intelligibility with vocoded Mandarin sentences. The three CI coding strategies, biologically-inspired hearing aid algorithm (BioAid), envelope enhancement (EE), and fundamental frequency modulation (F0mod), were combined with the advanced combination encoder (ACE) strategy. Hence, four singular coding strategies and four combinational coding strategies were derived. Mandarin sentences with speech-shape noise were processed using these coding strategies. Speech understanding of vocoded Mandarin sente...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - November 26, 2021 Category: Neuroscience Source Type: research

Classification of Motor Impairments of Post-Stroke Patients Based on Force Applied to a Handrail
This study is focused on the sit-to-stand motion of post-stroke patients because it is an important daily activity. Our previous study utilized muscle synergies (synchronized muscle activation) to classify the degree of motor impairment in patients and proposed appropriate rehabilitation methodologies. However, in our previous study, the patient was required to attach electromyography sensors to his/her body; thus, it was difficult to evaluate motor ability in daily circumstances. Here, we developed a handrail-type sensor that can measure the force applied to it. Using temporal features of the force data, the relationship ...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - November 26, 2021 Category: Neuroscience Source Type: research

A Center of Mass Estimation and Control Strategy for Body-Weight-Support Treadmill Training
Walking disorders are common in post-stroke. Body weight support (BWS) systems have been proposed and proven to enhance gait training systems for recovering in individuals with hemiplegia. However, the fixed weight support and walking speed increase the risk of falling and decrease the active participation of the subjects. This paper proposes a strategy to enhance the efficiency of BWS treadmill training. It consists in regulating the height of the BWS system to track the height of the subject’s center of mass (CoM), whereby the CoM is estimated through a long-short term memory (LSTM) network and a locomotion recogni...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - November 19, 2021 Category: Neuroscience Source Type: research

Asymmetry of Regional Phase Synchrony Cortical Networks Under Cognitive Alertness and Vigilance Decrement States
This study investigates intra-regional connectivity and regional hemispheric asymmetry under two vigilance states: alertness and vigilance decrement. The vigilance states were induced on nine healthy subjects while performing 30 min in-congruent Stroop color-word task (I-SCWT). We measured brain activity using Electroencephalography (EEG) signals with 64-channels. We quantified the regional network connectivity using the phase-locking value (PLV) with graph theory analysis (GTA) and Support Vector Machines (SVM). Results showed that the vigilance decrement state was associated with impaired information processing within th...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - November 19, 2021 Category: Neuroscience Source Type: research

Objective Assessment of Progression and Disease Characterization of Friedreich Ataxia via an Instrumented Drinking Cup: Preliminary Results
The monitoring of disease progression in certain neurodegenerative conditions can significantly be quantified with the help of objective assessments. The severity assessment of diseases like Friedreich ataxia (FRDA) are usually based on different subjective measures. The ability of a participant with FRDA to perform standard neurological tests is the most common way of assessing disease progression. In this feasibility study, an Ataxia Instrumented Measurement-Cup (AIM-C) is proposed to quantify the disease progression of 10 participants (mean age 39 years, onset of disease 16.3 years) in longitudinal timepoints. The devic...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - November 19, 2021 Category: Neuroscience Source Type: research

Inhibiting Spasticity by Blocking Nerve Signal Conduction in Rats With Spinal Cord Transection
Spasticity is a common motor disorder following a variety of upper motor neuron lesions that seriously affects the quality of patient’s life. We aimed to evaluate whether muscle spasms can be suppressed by blocking nerve signal conduction. A rat model of lower limb spasm was prepared and the conduction of pathological nerve signals were blocked to study the inhibitory effect of nerve signal block on muscle spasm. The experimental results showed that 4 weeks after the 9th segment of the rat’s thoracic spinal cord was completely transacted, the ${H}/{M}$ -ratio of the lower limbs increased, and rate-dependent dep...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - November 19, 2021 Category: Neuroscience Source Type: research

Evaluation of Synergy-Based Hand Gesture Recognition Method Against Force Variation for Robust Myoelectric Control
In this study, we proposed a force-invariant intent recognition method based on muscle synergy analysis (MSA) in the setting of three self-defined force levels (low, medium, and high). Specifically, a fast matrix factorization algorithm based on alternating non-negativity constrained least squares (NMF/ANLS) was chosen to extract task-specific synergies associated with each of six hand gestures in the training stage; while for the testing samples, we used the non-negative least square (NNLS) method to estimate neural commands for movement classification. The performance of proposed method was compared with conventional pat...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - November 16, 2021 Category: Neuroscience Source Type: research

A Generative Model to Synthesize EEG Data for Epileptic Seizure Prediction
Conclusion: The performance of CESP shows that synthetic data acquired association between features and labels very well and by using the augmented data CESP predicted better than chance level for both datasets. Significance: The proposed DCGAN can be used to generate synthetic data to increase the prediction performance and to overcome good quality data scarcity issue. (Source: IEE Transactions on Neural Systems and Rehabilitation Engineering)
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - November 16, 2021 Category: Neuroscience Source Type: research

Dynamic Causal Modeling on the Identification of Interacting Networks in the Brain: A Systematic Review
Dynamic causal modeling (DCM) has long been used to characterize effective connectivity within networks of distributed neuronal responses. Previous reviews have highlighted the understanding of the conceptual basis behind DCM and its variants from different aspects. However, no detailed summary or classification research on the task-related effective connectivity of various brain regions has been made formally available so far, and there is also a lack of application analysis of DCM for hemodynamic and electrophysiological measurements. This review aims to analyze the effective connectivity of different brain regions using...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - November 16, 2021 Category: Neuroscience Source Type: research

A Biomimetic Circuit for Electronic Skin With Application in Hand Prosthesis
In this study, a novel biomimetic circuit for SA-I and RA-I afferents is proposed to functionally replicate the spiking response of the biological tactile afferents to indentation stimuli. The circuit has been designed, laid out, and simulated in TSMC 180nm CMOS technology with a 1.8V supply voltage. A pair of SA-I and RA-I afferent circuits consume $3.5mu text{W}$ of power. The occupied silicon area is $180mu text{m},,times 220mu text{m}$ for 32 afferents. To provide the inputs for circuit testing, a patch of skin with a grid of mechanoreceptors is simulated and tested by an edge stimulus presented at different orientatio...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - November 12, 2021 Category: Neuroscience Source Type: research

Non-Invasive Functional Evaluation of the Human Spinal Cord by Assessing the Peri-Spinal Neurovascular Network With Near Infrared Spectroscopy
Current medical care lacks an effective functional evaluation for the spinal cord. Magnetic resonance imaging and computed tomography mainly provide structural information of the spinal cord, while spinal somatosensory evoked potentials are limited by a low signal to noise ratio. We developed a non-invasive approach based on near-infrared spectroscopy in dual-wavelength (760 and 850 nm for deoxy- or oxyhemoglobin respectively) to record the neurovascular response (NVR) of the peri-spinal vascular network at the 7th cervical and 10th thoracic vertebral levels of the spinal cord, triggered by unilateral median nerve electric...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - November 12, 2021 Category: Neuroscience Source Type: research

Microelectrode Array With Integrated Pneumatic Channels for Dynamic Control of Electrode Position in Retinal Implants
Retinal prostheses are biomedical devices that directly utilize electrical stimulation to create an artificial vision to help patients with retinal diseases such as retinitis pigmentosa. A major challenge in the microelectrode array (MEA) design for retinal prosthesis is to have a close topographical fit on the retinal surface. The local retinal topography can cause the electrodes in certain areas to have gaps up to several hundred micrometers from the retinal surface, resulting in impaired, or totally lost electrode functions in specific areas of the MEA. In this manuscript, an MEA with dynamically controlled electrode po...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - November 12, 2021 Category: Neuroscience Source Type: research

Identification of the General Anesthesia Induced Loss of Consciousness by Cross Fuzzy Entropy-Based Brain Network
In this study, to track the loss of consciousness (LOC) induced by GA, we first developed the multi-channel cross fuzzy entropy method to construct the time-varying networks, whose temporal fluctuations were then explored and quantitatively evaluated. Thereafter, an algorithm was further proposed to detect the time onset at which patients lost their consciousness. The results clarified during the resting state, relatively stable fuzzy fluctuations in multi-channel network architectures and properties were found; by contrast, during the LOC period, the disrupted frontal-occipital connectivity occurred at the early stage, wh...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - November 12, 2021 Category: Neuroscience Source Type: research

Synergy-Based Neural Interface for Human Gait Tracking With Deep Learning
Neural information decomposed from electromyography (EMG) signals provides a new path of EMG-based human-machine interface. Instead of the motor unit decomposition-based method, this work presents a novel neural interface for human gait tracking based on muscle synergy, the high-level neural control information to collaborate muscle groups for performing movements. Three classical synergy extraction approaches include Principle Component Analysis (PCA), Factor Analysis (FA), and Nonnegative Matrix Factorization (NMF), are employed for muscle synergy extraction. A deep regression neural network based on the bidirectional ga...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - November 12, 2021 Category: Neuroscience Source Type: research

Research on the Method of Displaying the Contour Features of Image to the Visually Impaired on the Touch Screen
Conveying image information to the blind or visually impaired (BVI) is an important means to improve their quality of life. The touch screen devices used daily are the potential carriers for BVI to perceive image information through touch. However, touch screen devices also have the disadvantages of limited computing power and lack of rich tactile experience. In order to help BVI to access images conveniently through the touch screen, we built an image contour display system based on vibrotactile feedback. In this paper, an image smoothing algorithm based on convolutional neural network that can run quickly on the touch sc...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - November 5, 2021 Category: Neuroscience Source Type: research

Intensive In-Bed Sensorimotor Rehabilitation of Early Subacute Stroke Survivors With Severe Hemiplegia Using a Wearable Robot
The objectives of this study were to investigate the feasibility and effectiveness of early in-bed sensorimotor rehabilitation on acute stroke survivors with severe hemiplegia using a wearable ankle robot. Eighteen patients (9 in the study group and 9 in the control group) with severe hemiplegia and no active ankle movement were enrolled in acute/subacute phase post stroke. During a typical 3-week hospital stay, patients in the study group received ankle robot-guided in-bed training (50 minutes/session, 5 sessions/week), including motor relearning under real-time visual feedback of re-emerging motor output, strong passive ...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - November 5, 2021 Category: Neuroscience Source Type: research