[Front cover]
Presents the front cover for this issue of the publication. (Source: IEE Transactions on Neural Systems and Rehabilitation Engineering)
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 1, 2020 Category: Neuroscience Source Type: research

IEEE Transactions on Neural Systems and Rehabilitation Engineering information for authors
(Source: IEE Transactions on Neural Systems and Rehabilitation Engineering)
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 1, 2020 Category: Neuroscience Source Type: research

Determining User Intent of Partly Dynamic Shoulder Tasks in Individuals With Chronic Stroke Using Pattern Recognition
This study examines whether pattern recognition of sensor data can accurately identify user intent for 9 combinations of 1- and 2- degree-of-freedom shoulder tasks. Participants with stroke (n = 12) used their paretic and non-paretic arms, and healthy controls (n = 12) used their dominant arm to complete tasks on a lab-based robot involving combinations of abduction, adduction, and internal and external rotation of the shoulder. We examined the effect of arm (paretic, non-paretic), load level (25% vs 50% maximal voluntary torque), and dataset (electromyography, load cell, or combined) on classifier performance. Results sug...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 1, 2020 Category: Neuroscience Source Type: research

Application of Reinforcement Learning to Deep Brain Stimulation in a Computational Model of Parkinson’s Disease
Deep brain stimulation (DBS) has been proven to be an effective treatment to deal with the symptoms of Parkinson’s disease (PD). Currently, the DBS is in an open-loop pattern with which the stimulation parameters remain constant regardless of fluctuations in the disease state, and adjustments of parameters rely mostly on trial and error of experienced clinicians. This could bring adverse effects to patients due to possible overstimulation. Thus closed-loop DBS of which stimulation parameters are automatically adjusted based on variations in the ongoing neurophysiological signals is desired. In this paper, we present ...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 1, 2020 Category: Neuroscience Source Type: research

A Bayesian Shared Control Approach for Wheelchair Robot With Brain Machine Interface
To enhance the performance of the brain-actuated robot system, a novel shared controller based on Bayesian approach is proposed for intelligently combining robot automatic control and brain-actuated control, which takes into account the uncertainty of robot perception, action and human control. Based on maximum a posteriori probability (MAP), this method establishes the probabilistic models of human and robot control commands to realize the optimal control of a brain-actuated shared control system. Application on an intelligent Bayesian shared control system based on steady-state visual evoked potential (SSVEP)-based brain...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 1, 2020 Category: Neuroscience Source Type: research

Prediction of Ankle Dorsiflexion Moment by Combined Ultrasound Sonography and Electromyography
To provide an effective and safe therapy to persons with neurological impairments, accurate determination of their residual volitional ability is required. However, accurate measurement of the volitional ability, through non-invasive means (e.g., electromyography), is challenging due to signal interference from neighboring muscles or stimulation artifacts caused by functional electrical stimulation (FES). In this work, a new model-based intention detection method that combines signals from both surface electromyography (sEMG) and ultrasound (US) sonography to predict isometric volitional ankle dorsiflexion moment is propos...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 1, 2020 Category: Neuroscience Source Type: research

An Interpretable Performance Metric for Auditory Attention Decoding Algorithms in a Context of Neuro-Steered Gain Control
In a multi-speaker scenario, a hearing aid lacks information on which speaker the user intends to attend, and therefore it often mistakenly treats the latter as noise while enhancing an interfering speaker. Recently, it has been shown that it is possible to decode the attended speaker from the brain activity, e.g., recorded by electroencephalography sensors. While numerous of these auditory attention decoding (AAD) algorithms appeared in the literature, their performance is generally evaluated in a non-uniform manner. Furthermore, AAD algorithms typically introduce a trade-off between the AAD accuracy and the time needed t...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 1, 2020 Category: Neuroscience Source Type: research

Deep Channel-Correlation Network for Motor Imagery Decoding From the Same Limb
In this study, we aim to use deep learning methods to explore the ceiling of the decoding performance of three tasks: the resting state, the MI of right hand and right elbow. To represent the brain functional relationships, the correlation matrix that consists of correlation coefficients between electrodes (channels) was calculated as features. We proposed the Channel-Correlation Network to learn the overall representation among channels for classification. Ensemble learning was applied to integrate the output of multiple Channel-Correlation Networks. Our proposed method achieved the decoding accuracy of up to 87.03% in th...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 1, 2020 Category: Neuroscience Source Type: research

Utilizing High-Density Electroencephalography and Motion Capture Technology to Characterize Sensorimotor Integration While Performing Complex Actions
Studies of sensorimotor integration often use sensory stimuli that require a simple motor response, such as a reach or a grasp. Recent advances in neural recording techniques, motion capture technologies, and time-synchronization methods enable studying sensorimotor integration using more complex sensory stimuli and performed actions. Here, we demonstrate that prehensile actions that require using complex sensory instructions for manipulating different objects can be characterized using high-density electroencephalography and motion capture systems. In 20 participants, we presented stimuli in different sensory modalities (...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 1, 2020 Category: Neuroscience Source Type: research

Continuous Description of Human 3D Motion Intent Through Switching Mechanism
This study finds that the switching mechanism can improve both the model estimation accuracy and the completeness for executing complex tasks. (Source: IEE Transactions on Neural Systems and Rehabilitation Engineering)
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 1, 2020 Category: Neuroscience Source Type: research

A Low-Cost End-to-End sEMG-Based Gait Sub-Phase Recognition System
This study presents a low-cost but effective end-to-end sEMG-based gait sub-phase recognition system, which contains a wireless multi-channel signal acquisition device simultaneously collecting sEMG of thigh muscles and plantar pressure signals, and a novel neural network-based sEMG signal classifier combining long-short term memory (LSTM) with multilayer perceptron (MLP). We evaluated the system with subjects walking under five conditions: flat terrain at 5 km/h, flat terrain at 3 km/h, 20 kg backpack at 5 km/h, 20 kg shoulder bag at 5 km/h and 15° slope at 5 km/h. Experimental results show that the proposed method ac...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 1, 2020 Category: Neuroscience Source Type: research

A Case Study With Symbihand: An sEMG-Controlled Electrohydraulic Hand Orthosis for Individuals With Duchenne Muscular Dystrophy
With recent improvements in healthcare, individuals with Duchenne muscular dystrophy (DMD) have prolonged life expectancy, and it is therefore vital to preserve their independence. Hand function plays a central role in maintaining independence in daily living. This requires sufficient grip force and the ability to modulate it with no substantially added effort. Individuals with DMD have low residual grip force and its modulation is challenging and fatiguing. To assist their hand function, we developed a novel dynamic hand orthosis called SymbiHand, where the user’s hand motor intention is decoded by means of surface ...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 1, 2020 Category: Neuroscience Source Type: research

Reconfiguring Motor Circuits for a Joint Manual and BCI Task
This study provides insight into the neural activity that enables a dual-control brain-computer interface. (Source: IEE Transactions on Neural Systems and Rehabilitation Engineering)
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 1, 2020 Category: Neuroscience Source Type: research

Adaptive Neural Sliding-Mode Controller for Alternative Control Strategies in Lower Limb Rehabilitation
Research on control strategies for rehabilitation robots has gradually shifted from providing therapies with fixed, relatively stiff assistance to compelling alternatives with assistance or challenge strategies to maximize subject participation. These alternative control strategies can promote neural plasticity and, in turn, increase the potential for recovery of motor coordination. In this paper, we propose a control strategy that dynamically switches between assistance and challenge modes based on the user’s performance by amplifying or reducing the deviation between the user and the rehabilitation robot. For a sea...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 1, 2020 Category: Neuroscience Source Type: research

Effects of Nonstationarity on Muscle Force Signals Regularity During a Fatiguing Motor Task
Physiological signals present fluctuations that can be assessed from their temporal structure, also termed complexity. The complexity of a physiological signal is usually quantified using entropy estimators, such as Sample Entropy. Recent studies have shown a loss of force signal complexity with the development of neuromuscular fatigue. However, these studies did not consider the stationarity of the force signals which is an important prerequisite of Sample Entropy measurements. Here, we investigated the effect of the potential nonstationarity of force signals on the kinetics of neuromuscular fatigue-induced change in forc...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 1, 2020 Category: Neuroscience Source Type: research

The Effect of Optic Flow Speed on Active Participation During Robot-Assisted Treadmill Walking in Healthy Adults
This study aimed to investigate: 1) the effect of optic flow speed manipulation on active participation during robot-assisted treadmill walking (RATW), 2) the influence of the type of virtual environment, and 3) the level of motion sickness and enjoyment. Twenty-eight healthy older adults were randomized in two groups: “stimulus rich” Park group (50% male, 61± 6 year) and “stimulus poor” Hallway group (43% male, 62± 5 year). Subjects walked in the Lokomat with immersive virtual reality (VR) with a matched, slow and fast optic flow speed, each lasting 7 minutes. Active participation was...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 1, 2020 Category: Neuroscience Source Type: research

Gait Trajectory and Event Prediction from State Estimation for Exoskeletons During Gait
A real-time method is proposed to obtain a single, consistent probabilistic model to predict future joint angles, velocities, accelerations and jerks, together with the timing for the initial contact, foot flat, heel off and toe off events. In a training phase, a probabilistic principal component model is learned from normal walking, which is used in the online phase for state estimation and prediction. This is validated for normal walking and walking with an exoskeleton. Without exoskeleton, both joint trajectories and gait events are predicted without bias. With exoskeleton, the trajectory prediction is unbiased, but eve...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 1, 2020 Category: Neuroscience Source Type: research

Modeling and Design of the Automatic Stance Phase Lock (ASPL) Knee Joint Control Mechanism for Paediatric Users With Transfemoral Amputations
The 2-axes Automatic Stance Phase Lock (ASPL) stance control mechanism has been demonstrated to improve adult amputees’ mobility but has yet to be developed for the paediatric population. The overall objective for this work was to characterize the ASPL control mechanism with biomechanical modelling and design a 2-axes ASPL prosthetic knee joint suitable for children between the ages of 6 and 12 years. Paediatric anthropometric data and ASPL control mechanism performance characteristics established from adult ASPL knee users were utilized to develop paediatric-appropriate configurations of the ASPL stance control mech...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 1, 2020 Category: Neuroscience Source Type: research

Accurate Ambulatory Gait Analysis in Walking and Running Using Machine Learning Models
Wearable sensors have been proposed as alternatives to traditional laboratory equipment for low-cost and portable real-time gait analysis in unconstrained environments. However, the moderate accuracy of these systems currently limits their widespread use. In this paper, we show that support vector regression (SVR) models can be used to extract accurate estimates of fundamental gait parameters (i.e., stride length, velocity, and foot clearance), from custom-engineered instrumented insoles (SportSole) during walking and running tasks. Additionally, these learning-based models are robust to inter-subject variability, thereby ...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 1, 2020 Category: Neuroscience Source Type: research

Multivariate Analysis of Joint Motion Data by Kinect: Application to Parkinson’s Disease
Analysis of joint motion data (AJMD) by Kinect, such as velocity, has been widely used in many research fields, many of which focused on how one joint moves with another, namely bivariate AJMD. However, these studies might not accurately reflect the motor symptoms in patients. The human body can be divided into six widely accepted parts (head, trunk and four limbs), which are interrelated and interact with each other. Therefore, in this study we attempted to investigate how the major joints of one body part move with the ones in another body part, namely multivariate AJMD. For method illustration, the motion data of sit-to...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 1, 2020 Category: Neuroscience Source Type: research

The Variability of Psychophysical Parameters Following Surface and Subdermal Stimulation: A Multiday Study in Amputees
This study investigates the multiday variability of subdermal and surface stimulation. Electrical stimulation was delivered using either surface or fine wire electrodes placed right under the skin in eight amputees for seven consecutive days. The variability of psychophysical measurements, including detection threshold (DT), pain threshold (PT), dynamic range (DR), just noticeable difference (JND), Weber fraction (WF) and quality of evoked sensations, was evaluated using the coefficient of variation (CoV). In addition, the systematic change in the mean of the parameters across days was assessed in both stimulation modaliti...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 1, 2020 Category: Neuroscience Source Type: research

Wearable Sensor-Based Digital Biomarker to Estimate Chest Expansion During Sit-to-Stand Transitions–A Practical Tool to Improve Sternal Precautions in Patients Undergoing Median Sternotomy
Sternal precautions are a universal part of the discharge education for post-sternotomy patients to reduce the risk of sternal complications. However, they are always designed based on physical therapists’ or surgeons’ subjective judgment without any objective evidence. Thus, they could be overly restrictive to hinder the patients’ recovery, physically and psychologically. To fill this gap, this paper proposes a digital biomarker to estimate chest expansion during sit-to-stand transitions based on wearable inertial sensing and data fusion technologies. First, we carried out bench tests to evaluate the rel...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 1, 2020 Category: Neuroscience Source Type: research

Objective and Subjective Effects of a Passive Exoskeleton on Overhead Work
Overhead work is a frequent cause of shoulder work-related musculoskeletal disorders. Exoskeletons offering arm support have the potential to reduce shoulder strain, without requiring large scale reorganization of the workspace. Assessment of such systems however requires to take multiple factors into consideration. This paper presents a thorough in-lab assessment of PAEXO, a novel passive exoskeleton for arm support during overhead work. A list of evaluation criteria and associated performance metrics is proposed to cover both objective and subjective effects of the exoskeleton, on the user and on the task being performed...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 1, 2020 Category: Neuroscience Source Type: research

Perceptual and Objective Assessment of Envelope Enhancement for Children With Auditory Processing Disorder
This paper evaluated the performance of an envelope enhancement (EE) algorithm subjectively by children with auditory processing disorder (APD), and objectively through computational models. Speech intelligibility data was collected from children with APD, for unprocessed and envelope-enhanced speech in the presence of stationary and non-stationary background noise at different signal to noise ratios (SNRs), both with and without noise reduction (NR) algorithms as a front-end to the EE algorithm. Furthermore, intrusive and non-intrusive objective speech intelligibility metrics were derived to predict the perceptual impact ...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 1, 2020 Category: Neuroscience Source Type: research

TinyFSCV: FSCV for the Masses
The ability to monitor neurochemical dynamics in target brain regions with a high degree of temporal resolution has assisted researchers in investigating the pathogenesis, and pathophysiology of a variety of neurological and psychiatric disorders. Current systems for neurochemical monitoring are bulky or expensive, limiting widespread exploration of this research field and preventing large-scale parallel experimentation. In this paper, we present a new miniaturized research platform, the TinyFSCV system, which can be used to monitor dynamic changes in neurochemicals through Fast-Scan Cyclic Voltammetry (FSCV). This system ...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 1, 2020 Category: Neuroscience Source Type: research

Functional Network Alterations in Patients With Amnestic Mild Cognitive Impairment Characterized Using Functional Near-Infrared Spectroscopy
In this study, functional near-infrared spectroscopy (fNIRS), an affordable, robust and portable neuroimaging modality, was employed to characterize the functional network in aMCI patients. FNIRS data were collected from 16 healthy controls and 16 aMCI patients using a digits verbal span task. Functional networks were constructed from temporal hemodynamic response signals. Graph-based indices were then calculated from the constructed brain networks to assess global and regional differences between the groups. Results suggested that brain networks in aMCI patients were characterized with higher integration as well as higher...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 1, 2020 Category: Neuroscience Source Type: research

Speed of Rapid Serial Visual Presentation of Pictures, Numbers and Words Affects Event-Related Potential-Based Detection Accuracy
Rapid serial visual presentation (RSVP) based brain-computer interfaces (BCIs) can detect target images among a continuous stream of rapidly presented images, by classifying a viewer’s event related potentials (ERPs) associated with the target and non-targets images. Whilst the majority of RSVP-BCI studies to date have concentrated on the identification of a single type of image, namely pictures, here we study the capability of RSVP-BCI to detect three different target image types: pictures, numbers and words. The impact of presentation duration (speed) i.e., 100–200ms (5–10Hz), 200–300ms (3.3&ndash...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 1, 2020 Category: Neuroscience Source Type: research

An Investigation of Neural Stimulation Efficiency With Gaussian Waveforms
Conclusion: These findings challenge the claims of up to 60% energy savings with Gaussian like stimulation waveforms. The moderate energy savings achieved with the novel waveform are accompanied with considerable increases in maximal instantaneous power. Larger power sources would therefore be required, and this opposes the trend for implant miniaturization. Significance: This is the first study to comprehensively investigate stimulation efficiency of Gaussian waveforms. It sheds new light on the practical potential of such stimulation wavefo- ms. (Source: IEE Transactions on Neural Systems and Rehabilitation Engineering)
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 1, 2020 Category: Neuroscience Source Type: research

A Subject-Transfer Framework Based on Single-Trial EMG Analysis Using Convolutional Neural Networks
In recent years, electromyography (EMG)-based practical myoelectric interfaces have been developed to improve the quality of daily life for people with physical disabilities. With these interfaces, it is very important to decode a user’s movement intention, to properly control the external devices. However, improving the performance of these interfaces is difficult due to the high variations in the EMG signal patterns caused by intra-user variability. Therefore, this paper proposes a novel subject-transfer framework for decoding hand movements, which is robust in terms of intra-user variability. In the proposed frame...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 1, 2020 Category: Neuroscience Source Type: research

Anomaly Detection of Moderate Traumatic Brain Injury Using Auto-Regularized Multi-Instance One-Class SVM
Detection and quantification of functional deficits due to moderate traumatic brain injury (mTBI) is crucial for clinical decision-making and timely commencement of functional therapy. In this work, we explore magnetoencephalography (MEG) based functional connectivity features i.e. magnitude squared coherence (MSC) and phase lag index (PLI) to quantify synchronized brain activity patterns as a means to detect functional deficits. We propose a multi-instance one-class support vector machine (SVM) model generated from a healthy control population. Any dispersion from the decision boundary of the model would be identified as ...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 1, 2020 Category: Neuroscience Source Type: research

Hash Transformation and Machine Learning-Based Decision-Making Classifier Improved the Accuracy Rate of Automated Parkinson’s Disease Screening
In this study, spirals and straight lines in polar coordinates are used to extract polar expression features such as the key parameters deviation (cm) and accumulation angle (rad). These parameters are quantitative manner to scale the variations of functional tremors in normal control subjects and patients with Parkinson’s disease (PD) and essential tremor (ET). However, difficulty arises in using nonlinear polar expression features in the two-dimensional feature space to separate normal control subjects from those with PD and ET. To solve the nonlinear separable classification problem, hash transformation is used to...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 1, 2020 Category: Neuroscience Source Type: research

Effects of Rest-Break on Mental Fatigue Recovery Determined by a Novel Temporal Brain Network Analysis of Dynamic Functional Connectivity
Mental fatigue is growingly considered to be associated with functional brain dysconnectivity. Although conventional wisdom suggests that rest break is an effective countermeasure, the underlying neural mechanisms and how they modulate fatigue-related brain dysconnectivity is largely unknown. Here, we introduce an empirical method to examine the reorganization of dynamic functional connectivity (FC) in a two-session experiment where one session including a mid-task break (Rest) compared to a successive task design in the other session (No-rest). Temporal brain networks were estimated from 20 participants and the spatiotemp...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 1, 2020 Category: Neuroscience Source Type: research

Multiscale Community Detection in Functional Brain Networks Constructed Using Dynamic Time Warping
Previous studies have focused on the detection of community structures of brain networks constructed with resting-state functional magnetic resonance imaging (fMRI) data. Pearson correlation is often used to describe the connections between nodes in the construction of functional brain networks, which typically ignores the inherent timing and validity of fMRI time series. To solve this problem, this study applied the Dynamic Time Warp (DTW) algorithm to determine the correlation between two brain regions by comparing the synchronization and asynchrony of the time series. In addition, to determine the best community structu...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 1, 2020 Category: Neuroscience Source Type: research

A Dual-Modal Attention-Enhanced Deep Learning Network for Quantification of Parkinson’s Disease Characteristics
In this study, we implemented a novel gait evaluating approach to provide not only a binary classification of PD gaits and normal walking, but also a quantification of the PD gaits to relate them to the PD severity level. The proposed system is a dual-modal deep-learning-based model, where left and right gait is modeled separately by a convolutional neural network (CNN) followed by an attention-enhanced long short-term memory (LSTM) network. The left and right samples for model training and testing were segmented sequentially from multiple 1D vertical ground reaction force (VGRF) signals according to the detected gait cycl...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 1, 2020 Category: Neuroscience Source Type: research

Automated Assessment of Oral Diadochokinesis in Multiple Sclerosis Using a Neural Network Approach: Effect of Different Syllable Repetition Paradigms
Slow and irregular oral diadochokinesis represents an important manifestation of spastic and ataxic dysarthria in multiple sclerosis (MS). We aimed to develop a robust algorithm based on convolutional neural networks for the accurate detection of syllables from different types of alternating motion rate (AMR) and sequential motion rate (SMR) paradigms. Subsequently, we explored the sensitivity of AMR and SMR paradigms based on voiceless and voiced consonants in the detection of speech impairment. The four types of syllable repetition paradigms including /ta/, /da/, /pa/-/ta/-/ka/, and /ba/-/da/-/ga/ were collected from 120...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 1, 2020 Category: Neuroscience Source Type: research

Modulation of Speech-in-Noise Comprehension Through Transcranial Current Stimulation With the Phase-Shifted Speech Envelope
We presented 17 subjects with speech in noise coupled with simultaneous transcranial alternating current stimulation. The currents were based on the envelope of the target speech but shifted by different phases, as well as by two temporal delays of 100 ms and 250 ms. We also employed various control stimulations, and assessed the signal-to-noise ratio at which the subject understood half of the speech. We found that, at both latencies, speech comprehension is modulated by the phase of the current stimulation. However, the form of the modulation differed between the two latencies. Phase and latency of neurostimulation have ...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 1, 2020 Category: Neuroscience Source Type: research

Assessing Neural Compensation With Visuospatial Working Memory Load Using Near-Infrared Imaging
This study investigated the effects of increasing task load as a means to induce neural compensation through a novel visual working memory (VSWM) task using functional near-infrared spectroscopy (fNIRS). The bilateral prefrontal cortex (PFC) was explored due to its relevance in VSWM and neural compensation. A total of 31 healthy controls (HC), 12 patients with MCI and 18 patients with mild Alzheimer’s disease (mAD) were recruited. Although all groups showed sensitivity in terms of behavioral performance (i.e. score) towards increasing task load (level 1 to 3), only in MCI load effect on cortical response (as measured...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 1, 2020 Category: Neuroscience Source Type: research

The Study of Generic Model Set for Reducing Calibration Time in P300-Based Brain–Computer Interface
P300-based brain-computer interfaces (BCIs) provide an additional communication channel for individuals with communication disabilities. In general, P300-based BCIs need to be trained, offline, for a considerable period of time, which causes users to become fatigued. This reduces the efficiency and performance of the system. In order to shorten calibration time and improve system performance, we introduce the concept of a generic model set. We used ERP data from 116 participants to train the generic model set. The resulting set consists of ten models, which are trained by weighted linear discriminant analysis (WLDA). Twelv...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 1, 2020 Category: Neuroscience Source Type: research

Table of contents
(Source: IEE Transactions on Neural Systems and Rehabilitation Engineering)
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 1, 2020 Category: Neuroscience Source Type: research

IEEE Transactions on Neural Systems and Rehabilitation Engineering publication information
(Source: IEE Transactions on Neural Systems and Rehabilitation Engineering)
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 1, 2020 Category: Neuroscience Source Type: research

Front cover
(Source: IEE Transactions on Neural Systems and Rehabilitation Engineering)
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 1, 2020 Category: Neuroscience Source Type: research

A Lower Limb Phantom for Simulation and Assessment of Electromyography Technology
Electromyography signal processing approaches have traditionally been validated through computer simulations. Electromyography electrodes and systems are often not validated or have been validated on human subjects where there is no clear ground truth signal for comparison. We sought to develop a physical limb phantom for validation of electromyography hardware and signal processing approaches. We embedded pairs of wires within a conductive gelatin surrounding an artificial bone such that the antennae could broadcast identified ground truth signals. The ground truth signals can be simple sinusoids or more complex represent...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 1, 2019 Category: Neuroscience Source Type: research

Robotic Exoskeleton for Wrist and Fingers Joint in Post-Stroke Neuro-Rehabilitation for Low-Resource Settings
Robots have the potential to help provide exercise therapy in a repeatable and reproducible manner for stroke survivors. To facilitate rehabilitation of the wrist and fingers joint, an electromechanical exoskeleton was developed that simultaneously moves the wrist and metacarpophalangeal joints. The device was designed for the ease of manufacturing and maintenance, with specific considerations for countries with limited resources. Active participation of the user is ensured by the implementation of electromyographic control and visual feedback of performance. Muscle activity requirements, movement parameters, range of moti...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 1, 2019 Category: Neuroscience Source Type: research

Multichannel Nerve Stimulation for Diverse Activation of Finger Flexors
Conclusion: These results revealed the diversity of elicitable finger movements and muscle activations. The system redundancy can be explored to compensate for system instability due to fatigue or electrode shift. The outcomes can also enable the development of an automatic calibration of the stimulation. (Source: IEE Transactions on Neural Systems and Rehabilitation Engineering)
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 1, 2019 Category: Neuroscience Source Type: research

Use of Pelvic Corrective Force With Visual Feedback Improves Paretic Leg Muscle Activities and Gait Performance After Stroke
The purpose of this study was to examine the effects of combined pelvic corrective force and visual feedback during treadmill walking on paretic leg muscle activity and gait characteristics in individuals with post-stroke hemiparesis. Fifteen chronic stroke participants completed visual feedback only and combined pelvic corrective force and visual feedback conditions during treadmill walking. Each condition included: 1-minute baseline, 7-minute training with visual feedback only or additional pelvic corrective force, 1-minute post training, 1-minute standing break, and another 5-minute training. EMGs from the paretic leg m...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 1, 2019 Category: Neuroscience Source Type: research

Proof of Concept of an Assistive Robotic Arm Control Using Artificial Stereovision and Eye-Tracking
Assistive robotic arms have become popular to help users with upper limb disabilities achieve autonomy in their daily tasks, such as drinking and grasping objects in general. Usually, these robotic arms are controlled with an adapted joystick. Joysticks are user-friendly when it comes to a general approach to an object. However, they are not as intuitive when having to accurately approach an object, especially when obstacles are present. Alternatively, the combined use of artificial stereovision and eye-tracking seems to be a promising solution, as the user’s vision is usually dissociated from their upper limb disabi...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 1, 2019 Category: Neuroscience Source Type: research

Effect of Amplitude and Number of Repetitions of the Perturbation on System Identification of Human Balance Control During Stance
In this study we investigated the effect of the amplitude and number of repetitions on the identification of the neuromuscular controller (NMC). Healthy participants were asked to stand on a treadmill while small continuous support surface translations were applied in the form of a periodic multisine signal. The perturbation amplitude varied over seven conditions between 0.02 and 0.20 m peak-to-peak (ptp), where 6.5 repetitions of the multisine signal were applied for each amplitude, resulting in a trial length of 130 sec. For one of the conditions, 24 repetitions were recorded. The recorded external perturbation torque, b...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 1, 2019 Category: Neuroscience Source Type: research

Optimal Estimation of EMG Standard Deviation (EMG $sigma$ ) in Additive Measurement Noise: Model-Based Derivations and Their Implications
Typical electromyogram (EMG) processors estimate EMG signal standard deviation (EMG $sigma $ ) via moving average root mean square (RMS) or mean absolute value (MAV) filters, whose outputs are used in force estimation, prosthesis/orthosis control, etc. In the inevitable presence of additive measurement noise, some processors subtract the noise standard deviation from EMG RMS (or MAV). Others compute a root difference of squares (RDS)—subtract the noise variance from the square of EMG RMS (or MAV), all followed by taking the square root. Herein, we model EMG as an amplitude-modulated random process in additive measure...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 1, 2019 Category: Neuroscience Source Type: research

Intraoperative Responses May Predict Chronic Performance of Composite Flat Interface Nerve Electrodes on Human Femoral Nerves
Peripheral nerve cuff electrodes (NCEs) in motor system neuroprostheses can generate strong muscle contractions and enhance surgical efficiency by accessing multiple muscles from a single proximal location. Predicting chronic performance of high contact density NCEs based on intraoperative observations would facilitate implantation at locations that maximize selective recruitment, immediate connection of optimal contacts to implanted pulse generators (IPGs) with limited output channels, and initiation of postoperative rehabilitation as soon as possible after surgery. However, the stability of NCE intraoperative recruitment...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 1, 2019 Category: Neuroscience Source Type: research

IEEE Transactions on Neural Systems and Rehabilitation Engineering information for authors
Provides instructions and guidelines to prospective authors who wish to submit manuscripts. (Source: IEE Transactions on Neural Systems and Rehabilitation Engineering)
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 1, 2019 Category: Neuroscience Source Type: research