IEEE Neural Systems and Rehabilitation Engineering

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Front cover

Sat, 03/31/2018 - 22:00
Presents the front cover for this issue of the publication.

IEEE Transactions on Neural Systems and Rehabilitation Engineering publication information

Sat, 03/31/2018 - 22:00
Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Table of contents

Sat, 03/31/2018 - 22:00
Presents the table of contents for this issue of the publication.

Subject-Independent ERP-Based Brain–Computer Interfaces

Sat, 03/31/2018 - 22:00
Brain-computer interfaces (BCIs) are desirable for people to express their thoughts, especially those with profound disabilities in communication. The classification of brain patterns for each different subject requires an extensively time-consuming learning stage specific to that person, in order to reach satisfactory accuracy performance. The training session could also be infeasible for disabled patients as they may not fully understand the training instructions. In this paper, we propose a unified classification scheme based on ensemble classifier, dynamic stopping, and adaptive learning. We apply this scheme on the P300-based BCI, with the subject-independent manner, where no learning session is required for new experimental users. According to our theoretical analysis and empirical results, the harmonized integration of these three methods can significantly boost up the average accuracy from 75.00% to 91.26%, while at the same time reduce the average spelling time from 12.62 to 6.78 iterations, approximately to two-fold faster. The experiments were conducted on a large public dataset which had been used in other related studies. Direct comparisons between our work with the others' are also reported in details.

Cognitive Behavior Classification From Scalp EEG Signals

Sat, 03/31/2018 - 22:00
Electroencephalography (EEG) has become increasingly valuable outside of its traditional use in neurology. EEG is now used for neuropsychiatric diagnosis, neurological evaluation of traumatic brain injury, neurotherapy, gaming, neurofeedback, mindfulness, and cognitive enhancement training. The trend to increase the number of EEG electrodes, the development of novel analytical methods, and the availability of large data sets has created a data analysis challenge to find the “signal of interest” that conveys the most information about ongoing cognitive effort. Accordingly, we compare three common types of neural synchrony measures that are applied to EEG-power analysis, phase locking, and phase-amplitude coupling to assess which analytical measure provides the best separation between EEG signals that were recorded, while healthy subjects performed eight cognitive tasks-Hopkins Verbal Learning Test and its delayed version, Stroop Test, Symbol Digit Modality Test, Controlled Oral Word Association Test, Trail Marking Test, Digit Span Test, and Benton Visual Retention Test. We find that of the three analytical methods, phase-amplitude coupling, specifically theta (4-7 Hz)-high gamma (70-90 Hz) obtained from frontal and parietal EEG electrodes provides both the largest separation between the EEG during cognitive tasks and also the highest classification accuracy between pairs of tasks. We also find that phase-locking analysis provides the most distinct clustering of tasks based on their utilization of long-term memory. Finally, we show that phase-amplitude coupling is the least sensitive to contamination by intense jaw-clenching muscle artifact.

Functional Connectivity Analysis of Mental Fatigue Reveals Different Network Topological Alterations Between Driving and Vigilance Tasks

Sat, 03/31/2018 - 22:00
Despite the apparent importance of mental fatigue detection, a reliable application is hindered due to the incomprehensive understanding of the neural mechanisms of mental fatigue. In this paper, we investigated the topological alterations of functional brain networks in the theta band (4 - 7 Hz) of electroencephalography (EEG) data from 40 male subjects undergoing two distinct fatigue-inducing tasks: a low-intensity one-hour simulated driving and a high-demanding half-hour sustained attention task [psychomotor vigilance task (PVT)]. Behaviorally, subjects demonstrated a robust mental fatigue effect, as reflected by significantly declined performances in cognitive tasks prior and post these two tasks. Furthermore, characteristic path length presented a positive correlation with task duration, which led to a significant increase between the first and the last five minutes of both tasks, indicating a fatigue-related disruption in information processing efficiency. However, significantly increased clustering coefficient was revealed only in the driving task, suggesting distinct network reorganizations between the two fatigue-inducing tasks. Moreover, high accuracy (92% for driving; 97% for PVT) was achieved for fatigue classification with apparently different discriminative functional connectivity features. These findings augment our understanding of the complex nature of fatigue-related neural mechanisms and demonstrate the feasibility of using functional connectivity as neural biomarkers for applicable fatigue monitoring.

Contact Pressure and Flexibility of Multipin Dry EEG Electrodes

Sat, 03/31/2018 - 22:00
In state-of-the-art electroencephalography (EEG) Silver/Silver-Chloride electrodes are applied together with electrolyte gels or pastes. Their application requires extensive preparation, trained medical staff and limits measurement time and mobility. We recently proposed a novel multichannel cap system for dry EEG electrodes for mobile and out-of-the-lab EEG acquisition. During the tests with these novel polymer-based multipin dry electrodes, we observed that the quality of the recording depends on the applied normal force and resulting contact pressure. Consequently, in this paper we systematically investigate the influence of electrode-skin contact pressure and electrode substrate flexibility on interfacial impedance and perceived wearing comfort in a study on 12 volunteers. The normal force applied to the electrode was varied between the minimum required force to achieve impedances c1.3 MΩ and a maximum of 4 N, using a new force measurement applicator. We found that for a polymer shore hardness A98, with increasing normal force, the impedance decreases from 348 ± 236 kΩ and 257 ± 207 kΩ to 29 ± 14 kΩ and 23 ± 11 kΩ at frontal.

A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series

Sat, 03/31/2018 - 22:00
Sleep stage classification constitutes an important preliminary exam in the diagnosis of sleep disorders. It is traditionally performed by a sleep expert who assigns to each 30 s of the signal of a sleep stage, based on the visual inspection of signals such as electroencephalograms (EEGs), electrooculograms (EOGs), electrocardiograms, and electromyograms (EMGs). We introduce here the first deep learning approach for sleep stage classification that learns end-to-end without computing spectrograms or extracting handcrafted features, that exploits all multivariate and multimodal polysomnography (PSG) signals (EEG, EMG, and EOG), and that can exploit the temporal context of each 30-s window of data. For each modality, the first layer learns linear spatial filters that exploit the array of sensors to increase the signal-to-noise ratio, and the last layer feeds the learnt representation to a softmax classifier. Our model is compared to alternative automatic approaches based on convolutional networks or decisions trees. Results obtained on 61 publicly available PSG records with up to 20 EEG channels demonstrate that our network architecture yields the state-of-the-art performance. Our study reveals a number of insights on the spatiotemporal distribution of the signal of interest: a good tradeoff for optimal classification performance measured with balanced accuracy is to use 6 EEG with 2 EOG (left and right) and 3 EMG chin channels. Also exploiting 1 min of data before and after each data segment offers the strongest improvement when a limited number of channels are available. As sleep experts, our system exploits the multivariate and multimodal nature of PSG signals in order to deliver the state-of-the-art classification performance with a small computational cost.

Anomaly Detection of Electromyographic Signals

Sat, 03/31/2018 - 22:00
In this paper, we provide a robust framework to detect anomalous electromyographic (EMG) signals and identify contamination types. As a first step for feature selection, optimally selected Lawton wavelets transform is applied. Robust principal component analysis (rPCA) is then performed on these wavelet coefficients to obtain features in a lower dimension. The rPCA based features are used for constructing a self-organizing map (SOM). Finally, hierarchical clustering is applied on the SOM that separates anomalous signals residing in the smaller clusters and breaks them into logical units for contamination identification. The proposed methodology is tested using synthetic and real world EMG signals. The synthetic EMG signals are generated using a heteroscedastic process mimicking desired experimental setups. A sub-part of these synthetic signals is introduced with anomalies. These results are followed with real EMG signals introduced with synthetic anomalies. Finally, a heterogeneous real world data set is used with known quality issues under an unsupervised setting. The framework provides recall of 90% (± 3.3) and precision of 99%(±0.4).

Motor Skill Development Alters Kinematics and Co-Activation Between Flexors and Extensors of Limbs in Human Infant Crawling

Sat, 03/31/2018 - 22:00
Hands and knees crawling is an important motor developmental milestone but the current clinical measures of motor function during crawling stage are relatively subjective. Objective metrics using kinematics and electromyography (EMG) in infant crawling may provide more stable and accurate measures of such developmental milestone, demonstrating changes in locomotion during age span. The purpose of this paper was to determine whether joint kinematics and the underlying co-activation between flexor and extensor in infant crawling are different for arms and legs across the infant age span. Surface EMG of two pairs of flexors and extensors from arms and legs and the corresponding joint kinematic data were collected in twenty health infants (11 males and 9 females, range 8-15 months), while they were crawling on hands and knees. Co-activation index of averaged EMG was used to quantify the simultaneous contractions between flexor and extensor muscles. Coefficient of variation of joint's maximum vertical acceleration from multiple cycles was used to quantify the repeatability of kinematics during crawling. Our results indicated that the arm exhibited significantly higher co-activation and higher repeatability of joint movement than the leg, suggesting earlier development of arm compared to leg. Moreover, elder age groups, who had stronger walking ability developed, showed increased co-activation of the leg and significant increase in repeatability of the knee movement. These results were consistent with the rapid reinforcement of the leg during motor development from quadrupeds to bipedal walking. Furthermore, the EMG and kinematic parameters were significantly correlated with clinical variables. These results suggest that the EMG and kinematic analysis of infant crawling are useful in building effective assessment of infant's motor function before independent walking.

A Unified Controller for Walking on Even and Uneven Terrain With a Powered Ankle Prosthesis

Sat, 03/31/2018 - 22:00
This paper describes the development of a controller for a powered ankle prosthesis that is intended to provide appropriate biomechanical behavior for walking on both even and uneven terrain without having to explicitly detect local slope to do so. In order to inform development of the controller, the authors conducted a small study of five healthy subjects walking on even and uneven terrain. Data from the healthy subject study were used to formulate behavioral models for the healthy ankle, which were then implemented as controller behaviors in the powered prosthesis prototype and comparatively assessed on an amputee subject.

Usability and Validation of the Smarter Balance System: An Unsupervised Dynamic Balance Exercises System for Individuals With Parkinson’s Disease

Sat, 03/31/2018 - 22:00
Conventional physical and balance rehabilitation programs to improve balance performance and increase postural stability are often limited due to cost, availability of physical therapists, and accessibility to rehabilitation facilities. Exercise compliance is also affected by a loss of memory and decline in motivation in prescribed home-based balance training. We have developed the smarter balance system (SBS) incorporating multimodal biofeedback (visual plus vibrotactile) intended for clinical and home-based balance rehabilitation and assessed its efficacy on physical therapists' recommended dynamic weight-shifting balance exercises (dynamic WSBE) in individuals with Parkinson's disease (PD). The SBS consists of a smartphone and custom belt housing a processing unit, miniaturized sensors, and vibrating actuators (tactors). Visual and vibrotactile biofeedback guidance during dynamic WSBE is generated by the SBS's custom app based on 90% of the user's limits of stability (LOS). Ten individuals with idiopathic PD having impaired postural stability participated in one unsupervised session comprising 24 trials of the dynamic WSBE in a laboratory setting. Participants' limits of stability (LOS) in the anterior-posterior (A/P) and medial-lateral (M/L) direction were measured at the pre- and post-session. To assess the efficacy of SBS to provide guidance during balance rehabilitation using dynamic WSBE, cross-correlation (XCOR), position error (PE), and percent of tactor activation (PTA) were measured. There was a significant increase in LOS between the pre- and post-training session in both A/P and M/L directions. The average XCOR across all participants were 0.87 (SD = 0.11) and 0.76 (SD = 0.11) for the A/P and M/L direction respectively. The average PE and PTA for the A/P direction was 1.17 deg (SD = 0.60) and 65.35% (SD = 15.1) respectively and 0.74 deg (SD = 0.28) and 31.3% (SD = 16.42) in the M/L direction respectively. There was no significant effect of trials- for XCOR, PE, and PTA. Participants' LOS significantly increased after one session of the dynamic WSBE. Individuals with PD could accurately follow the target movements during the dynamic WSBE using the SBS. Future studies will assess the efficacy and acceptability of the SBS during long-term in-home rehabilitative training for balance-impaired individuals.

EMG-Torque Dynamics Change With Contraction Bandwidth

Sat, 03/31/2018 - 22:00
An accurate model for ElectroMyoGram (EMG)-torque dynamics has many uses. One of its applications which has gained high attention among researchers is its use, in estimating the muscle contraction level for the efficient control of prosthesis. In this paper, the dynamic relationship between the surface EMG and torque during isometric contractions at the human ankle was studied using system identification techniques. Subjects voluntarily modulated their ankle torque in dorsiflexion direction, by activating their tibialis anterior muscle, while tracking a pseudo-random binary sequence in a torque matching task. The effects of contraction bandwidth, described by torque spectrum, on EMG-torque dynamics were evaluated by varying the visual command switching time. Nonparametric impulse response functions (IRF) were estimated between the processed surface EMG and torque. It was demonstrated that: 1) at low contraction bandwidths, the identified IRFs had unphysiological anticipatory (i.e., non-causal) components, whose amplitude decreased as the contraction bandwidth increased. We hypothesized that this non-causal behavior arose, because the EMG input contained a component due to feedback from the output torque, i.e., it was recorded from within a closed-loop. Vision was not the feedback source since the non-causal behavior persisted when visual feedback was removed. Repeating the identification using a nonparametric closed-loop identification algorithm yielded causal IRFs at all bandwidths, supporting this hypothesis. 2) EMG-torque dynamics became faster and the bandwidth of system increased as contraction modulation rate increased. Thus, accurate prediction of torque from EMG signals must take into account the contraction bandwidth sensitivity of this system.

Differential Inverse Kinematics of a Redundant 4R Exoskeleton Shoulder Joint

Sat, 03/31/2018 - 22:00
Most active upper-extremity rehabilitation exoskeleton designs incorporate a three sequential rotational shoulder joint with orthogonal axes. This kind of joint has poor conditioning close to singular configurations when all joint axes become coplanar, which reduces its effective range of motion. We investigate an alternative approach of using a redundant non-orthogonal 4R shoulder joint. By inspecting the behavior of the possible nullspace motions, a new method is devised to resolve the redundancy in the differential inverse kinematics (IK) problem. A 1D nullspace global attraction method is used, instead of naive nullspace projection, to guarantee proper convergence. The design of the exoskeleton and the proposed IK method ensure good conditioning, avoid collisions with the human head, arm and trunk, can reach the entire human workspace, and outperforms conventional 3R orthogonal exoskeleton designs in terms of lower joint velocities and no body collisions.

An Optimal Method of Training the Specific Lower Limb Muscle Group Using an Exoskeletal Robot

Sat, 03/31/2018 - 22:00
This paper suggests a novel method of strengthening specific muscle groups in the lower limb during a functional movement. When the foot of an user wearing an exoskeletal robot follows a given path, the contribution of each muscle group to generate the motion changes along the path of the trajectory. The efficiency of muscle training, which is defined as the ratio of the work of a specific muscle group to that of all groups, can be maximized by changing the training load along the path. Based on a musculoskeletal model, the contribution of each muscle group along the path can be calculated as a function of its position. When a specific muscle group is chosen for exercise or rehabilitation, the efficiency of training can be maximized by setting high load where its contribution is high and low load where its contribution is low. By doing so, the user can exercise longer with the same amount of energy consumption. The EXOWheel that features a lower limb exoskeleton is employed to verify the method and the hamstring muscle group is selected as the specific muscle group. Three healthy subjects participate in the experiment, and electromyogram sensors are employed to monitor the muscle power. The results indicate that the efficiency of the hamstring muscle group for a given circular foot path under the optimal training load is 46.2% compared with 32.5% with a constant load and it means the 42% higher efficiency of the specific muscle group training.

Focal Vibration Stretches Muscle Fibers by Producing Muscle Waves

Sat, 03/31/2018 - 22:00
Focal vibration is an effective intervention for the management of spasticity. However, its neuromechanical effects, particularly how tonic vibration reflex is induced explicitly, remain implicit. In this paper, we utilize a high-speed camera and a method of image processing to quantify the muscle vibration rigorously and disclose the neuromechanical mechanism of focal vibration. The vibration of 75 Hz is applied on the muscle belly of the biceps brachii and muscle responses are captured by a high-speed camera in profile. The muscle silhouettes are identified by the Canny edge detector to represent the stretch of muscle fibers, and the consistency between the muscle stretch and profile deformation has been confirmed by the magnetic resonance imaging in advance. Oscillations of muscle points discretized by pixels are identified by the fast Fourier transformation, respectively, and results demonstrate that focal vibration stretches muscle by producing muscle waves. Specifically, each point vibrates harmonically, and, given the linear phase modulation with transverse position, the muscle vibration propagates as traveling waves. The propagation of muscle waves is associated with muscle stretch, whose frequency is the same with the vibrator due to the curved baseline, and thus induces the tonic vibration reflex via spinal circuits.

Retraining of Human Gait - Are Lightweight Cable-Driven Leg Exoskeleton Designs Effective?

Sat, 03/31/2018 - 22:00
Exoskeletons for gait training commonly use a rigid-linked “skeleton” which makes them heavy and bulky. Cable-driven exoskeletons eliminate the rigid-linked skeleton, providing a lighter and transparent design. Current cable-driven exoskeletons are aimed only at gait assistance by providing short bursts of forces to the leg during walking. It has not yet been shown if these designs are suitable for gait retraining, where rehabilitative forces need to be continuously applied to the leg in response to errors from a desired movement. The goal of this study is to investigate if a cable-driven leg exoskeleton can retrain the gait of human users. Nine healthy subjects were trained by a cable-driven leg exoskeleton to walk in a new gait pattern with 30% increase in step height from their natural gait. After 40 min of training, the gait of the subjects became significantly closer to the target gait than before the training. In three different post-training sessions, the step height of the subjects increased by 22%, 29%, and 31% on an average. In a fourth post-training session, when the subjects were instructed to ignore the training and walk naturally, the step height remained increased by 11%. These results confirm the potential of cable-driven designs in gait training applications.

Simultaneous Recognition and Assessment of Post-Stroke Hemiparetic Gait by Fusing Kinematic, Kinetic, and Electrophysiological Data

Sat, 03/31/2018 - 22:00
Gait analysis for the patients with lower limb motor dysfunction is a useful tool in assisting clinicians for diagnosis, assessment, and rehabilitation strategy making. Implementing accurate automatic gait analysis for the hemiparetic patients after stroke is a great challenge in clinical practice. This study is to develop a new automatic gait analysis system for qualitatively recognizing and quantitatively assessing the gait abnormality of the post-stroke hemiparetic patients. Twenty-one post-stroke patients and twenty-one healthy volunteers participated in the walking trials. Three of the most representative gait data, i.e., marker trajectory (MT), ground reaction force (GRF), and electromyogram, were simultaneously acquired from these subjects during their walking. A multimodal fusion architecture is established by using these different modal data to qualitatively distinguish the hemiparetic gait from normal gait by different pattern recognition techniques and to quantitatively estimate the patient's lower limb motor function by a novel probability-based gait score. Seven decision fusion algorithms have been tested in this architecture, and extensive data analysis experiments have been conducted. The results indicate that the recognition performance and estimation performance of the system become better when more modal gait data are fused. For the recognition performance, the random forest classifier based on the GRF data achieves an accuracy of 92.26% outperformed other single-modal schemes. When combining two modal data, the accuracy can be enhanced to 95.83% by using the support vector machine (SVM) fusion algorithm to fuse the MT and GRF data. When integrating all the three modal data, the accuracy can be further improved to 98.21% by using the SVM fusion algorithm. For the estimation performance, the absolute values of the correlation coefficients between the estimation results of the above three schemes and the Wisconsin gait scale scores for the post-str- ke patients are 0.63, 0.75, and 0.84, respectively, which means the clinical relevance becomes more obvious when using more modalities. These promising results demonstrate that the proposed method has considerable potential to promote the future design of automatic gait analysis systems for clinical practice.

Synthesis of Subject-Specific Human Balance Responses Using a Task-Level Neuromuscular Control Platform

Sat, 03/31/2018 - 22:00
Many activities of daily living require a high level of neuromuscular coordination and balance control to avoid falls. Complex musculoskeletal models paired with detailed neuromuscular simulations complement experimental studies and uncover principles of coordinated and uncoordinated movements. Here, we created a closed-loop forward dynamic simulation framework that utilizes a detailed musculoskeletal model (19 degrees of freedom, and 92 muscles) to synthesize human balance responses after support-surface perturbation. In addition, surrogate response models of task-level experimental kinematics from two healthy subjects were provided as inputs to our closed-loop simulations to inform the design of the task-level controller. The predicted muscle activations and the resulting synthesized subject joint angles showed good conformity with the average of experimental trials. The simulated whole-body center of mass displacements, generated from a single kinematics trial per perturbation direction, were on average, within 7 mm (anterior perturbations) and 13 mm (posterior perturbations) of experimental displacements. Our results confirmed how a complex subject-specific movement can be reconstructed by sequencing and prioritizing multiple task-level commands to achieve desired movements. By combining the multidisciplinary approaches of robotics and biomechanics, the platform demonstrated here offers great potential for studying human movement control and subject-specific outcome prediction.

A Noninvasive Brain-Computer Interface for Real-Time Speech Synthesis: The Importance of Multimodal Feedback

Sat, 03/31/2018 - 22:00
We conducted a study of a motor imagery brain-computer interface (BCI) using electroencephalography to continuously control a formant frequency speech synthesizer with instantaneous auditory and visual feedback. Over a three-session training period, sixteen participants learned to control the BCI for production of three vowel sounds (/i/ [heed], /A/ [hot], and /u/ [who'd]) and were split into three groups: those receiving unimodal auditory feedback of synthesized speech, those receiving unimodal visual feedback of formant frequencies, and those receiving multimodal, audio-visual(AV) feedback.Audio feedback was provided by a formant frequency artificial speech synthesizer, and visual feedback was given as a 2-D cursor on a graphical representation of the plane defined by the first two formant frequencies. We found that combined AV feedback led to the greatest performance in terms of percent accuracy, distance to target, and movement time to target compared with either unimodal feedback of auditory or visual information. These results indicate that performance is enhanced when multimodal feedback is meaningful for the BCI task goals, rather than as a generic biofeedback signal of BCI progress.

HIVE is supported by the European Commission under the Future and Emerging Technologies program.

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