IEEE Neural Systems and Rehabilitation Engineering

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Front cover

Fri, 11/30/2018 - 23:00
Presents the front cover for this issue of the publication.

IEEE Transactions on Neural Systems and Rehabilitation Engineering publication information

Fri, 11/30/2018 - 23:00
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

Fri, 11/30/2018 - 23:00
Presents the table of contents for this issue of the publication.

Sleep Quality Estimation by Cardiopulmonary Coupling Analysis

Fri, 11/30/2018 - 23:00
The gold standard for assessment of sleep quality is the polysomnography, where physiological signals are used to generate both quantitative and qualitative measurements. Despite the production of highly accurate results, polysomnography is a complex, uncomfortable, and expensive process, inaccessible to a large group of the population. Home monitoring devices were developed to address these issues, fitting the growing perspective of health care and focusing on prevention and wellness. The objective of this paper was to develop an algorithm capable of estimating the quality of sleep, by analyzing the cyclic alternating pattern rate. The algorithm uses a single-lead electrocardiogram to produce a spectrographic measure of the cardiopulmonary coupling that in turn was fed to a classifier to estimate the non-rapid eye movement sleep and the presence of the cyclic alternating pattern. Two classifiers were tested, a feedforward neural network and a deeply stacked autoencoder, with the second achieving better results, correctly classifying 77% of the subjects sleep quality (either good or bad). The developed method can be implemented in a home monitoring device to estimate the sleep quality in a non-invasive way and improve the detection of pathologies.

Single-Finger Neural Basis Information-Based Neural Decoder for Multi-Finger Movements

Fri, 11/30/2018 - 23:00
In this paper, we investigate the relationship between single and multi-finger movements. By exploiting the neural correlation between the temporal firing patterns between movements, we show that the Pearson’s correlation coefficient for the physically related movement pairs are greater than those of others; the firing rates of the neurons that are tuned to a single-finger movements also increases when the corresponding multi-finger movements are instructed. We also use a hierarchical cluster analysis to verify not only the relationship between the single and multi-finger movements, but also the relationship between the flexion and extension movements. Furthermore, we propose a novel decoding method of modeling neural firing patterns while omitting the training process of the multi-finger movements. For the decoding, the Skellam and Gaussian probability distributions are used as mathematical models. The probabilistic distribution model of the multi-finger movements was estimated using the neural activity that was acquired during single-finger movements. As a result, the proposed neural decoding accuracy comparable with that of the supervised neural decoding accuracy when all of the neurons were used for the multi-finger movements. These results suggest that only the neural activities of single-finger movements can be exploited for the control of dexterous multi-finger neuroprosthetics.

Bayesian Optimized Spectral Filters Coupled With Ternary ECOC for Single-Trial EEG Classification

Fri, 11/30/2018 - 23:00
Motivated by the promising emergence of brain–computer interfaces (BCIs) within assistive/rehabilitative systems for therapeutic applications, this paper proposes a novel Bayesian framework that simultaneously optimizes a number of subject-specific filter banks and spatial filters. Optimized double-band spectro-spatial filters are derived based on common spatial patterns coupled with the error-correcting output coding (ECOC) classifiers. The proposed framework constructs optimized subject-specific spectral filters in an intuitive fashion resulting in creation of significantly discriminant features, which is a crucial requirement for any EEG-based BCI system. Through incorporation of the ECOC approach, the classification problem is then modeled as communication over a noisy channel where the misclassification error is corrected by error correction techniques borrowed from an information theory. This paper also proposes a modified version of the ECOC adopted to EEG classification problems by deploying ternary class codewords to increase the Hamming distance between the codewords and introduce more robustness to misclassification error. The proposed framework is evaluated over two different datasets from the BCI Competition (i.e., BCIC- $textit {IV}_{textsf {2}{a}}$ and BCIC- $textit {IV}_{textsf {2}{b}}$ ). The results indicate that the proposed approach outperforms its counterparts and validate the essential role of optimized spectral filters on the overall classification accuracy.

Predicting Microsleep States Using EEG Inter-Channel Relationships

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A microsleep is a brief and an involuntary sleep-related loss of consciousness of up to 15 s. We investigated the performances of seven pairwise inter-channel relationships–covariance, Pearson’s correlation coefficient, wavelet cross-spectral power, wavelet coherence, joint entropy, mutual information, and phase synchronization index–in continuous prediction of microsleep states from EEG. These relationships were used as the feature sets of a linear discriminant analysis (LDA) and a linear support vector machine classifiers. Priors for both classifiers were incorporated to address the class imbalance in the training data sets. Each feature set was extracted from a 5-s window of EEG with the step of 0.25 s and was demeaned with respect to the mean of first 2 min. The sequential forward selection (SFS) method, based on a serial combination of the correlation coefficient, Fisher score-based filter, and an LDA-based wrapper, was used to select features from each training set. The comparison was based on 16-channel EEG data from eight subjects who had performed a 1-D visuomotor task for two 1-h sessions. The prediction performances were evaluated using leave-one-subject-out cross-validation. For both classifiers, non-normalized feature sets were found to perform better than normalized feature sets. Furthermore, demeaning the non-normalized features considerably improved the prediction performance. Overall, the LDA classifier with joint entropy features resulted in the best average prediction performances (phi, AUCPR, and AUCROC) of (0.47, 0.50, and 0.95). Joint entropy between O1 and O2 from theta frequency band was the most informative feature.

Noise-Assisted Multivariate EMD-Based Mean-Phase Coherence Analysis to Evaluate Phase-Synchrony Dynamics in Epilepsy Patients

Fri, 11/30/2018 - 23:00
Spatiotemporal evolution of synchrony dynamics among neuronal populations plays an important role in decoding complicated brain function in normal cognitive processing as well as during pathological conditions such as epileptic seizures. In this paper, a non-linear analytical methodology is proposed to quantitatively evaluate the phase-synchrony dynamics in epilepsy patients. A set of finite neuronal oscillators was adaptively extracted from a multi-channel electrocorticographic (ECoG) dataset utilizing noise-assisted multivariate empirical mode de-composition (NA-MEMD). Next, the instantaneous phases of the oscillatory functions were extracted using the Hilbert transform in order to be utilized in the mean-phase coherence analysis. The phase-synchrony dynamics were then assessed using eigenvalue decomposition. The extracted neuronal oscillators were grouped with respect to their frequency range into wideband (1–600 Hz), ripple (80–250 Hz), and fast-ripple (250–600 Hz) bands in order to investigate the dynamics of ECoG activity in these frequency ranges as seizures evolve. Drug-refractory patients with frontal and temporal lobe epilepsy demonstrated a reduction in phase-synchrony around seizure onset. However, the network phase-synchrony started to increase toward seizure end and achieved its maximum level at seizure offset for both types of epilepsy. This result suggests that hyper-synchronization of the epileptic network may be an essential self-regulatory mechanism by which the brain terminates seizures.

A New Unsupervised Detector of High-Frequency Oscillations in Accurate Localization of Epileptic Seizure Onset Zones

Fri, 11/30/2018 - 23:00
This paper presents a new unsupervised detector for automatically detecting high-frequency oscillations (HFOs) using intracranial electroencephalogram (iEEG) signals. This detector does not presuppose a specific number of clusters and has a good performance. First, the HFO candidates are detected by an initial detection method which distinguishes HFOs from background activities. Then, as significant features, fuzzy entropy, short-time energy, power ratio, and spectral centroid of the HFO candidates are investigated and constructed as a feature vector. Finally, the feature vector is used as the input of the fuzzy- ${c}$ -means-quantization-error-modeling-based expectation–maximization-Gaussian mixture model clustering algorithm. This algorithm has the advantages of detecting HFOs and avoiding false detection caused by artifacts. The concentrations of detected HFOs are used to localize epileptic seizure onset zones in epileptic iEEG signal analysis. A comparison shows that our detector provides better localization performance in terms of sensitivity and specificity than five existing detectors.

A Brain–Computer Interface-Based Action Observation Game That Enhances Mu Suppression

Fri, 11/30/2018 - 23:00
Action observation training based on the theory of activation of the mirror-neuron system has been used for the rehabilitation of patients with stroke. In this paper, we sought to assess whether a brain–computer interface (BCI)-based action observation rehabilitation game, using a flickering action video, could preferentially activate the mirror-neuron system. Feedback of stimulus observation, evoked by the flickering action video, was provided using steady state visually evoked potential and event-related desynchronization. Fifteen healthy subjects have experienced the game with BCI interaction (game and interaction), without BCI interaction (game without interaction), observed non-flickering stimuli, and flickering stimuli without the game background (stimuli only) in a counter-balanced order. The game and interface condition was resulted in significantly stronger activation of the mirror-neuron system than did the other three conditions. In addition, the amount of mirror-neuron system activation is gradually decreased in the game without interface, non-flickering stimuli, and stimuli only conditions in a time-dependent manner; however, in the game and interface condition, the amount of mirror-neuron system activation was maintained until the end of the training. Taken together, these data suggest that the proposed game paradigm, which integrates the action observation paradigm with BCI technology, could provide interactive responses for whether watching video clips can engage patients and enhance rehabilitation.

Progressive Thresholding: Shaping and Specificity in Automated Neurofeedback Training

Fri, 11/30/2018 - 23:00
Neurofeedback has long been proposed as a promising form of adjunctive non-pharmaceutical treatment for a variety of neuropsychological disorders. However, there is much debate over its efficacy and specificity. Many suggest that specificity can only be achieved when a specially trained clinician manually updates reward thresholds that indicate to the trainee when they are modulating their brain activity correctly, during training. We present a novel fully automated reward thresholding algorithm called progressive thresholding and test it with a frontal alpha asymmetry neurofeedback protocol. Progressive thresholding uses dynamic difficulty tuning and individual-specific progress models to simulate the shaping a clinician might perform when setting reward thresholds manually. We demonstrate in a double-blind comparison that progressive thresholding leads to significantly better learning outcomes compared with current automatic reward thresholding algorithms.

Assessment of the Complex Refractive Indices of Xenopus Laevis Sciatic Nerve for the Optimization of Optical (NIR) Neurostimulation

Fri, 11/30/2018 - 23:00
Despite an increasing interest in the use of light for neural stimulation, there is little information on how it interacts with neural tissue. The choice of wavelength in most of the optical stimulation literature is based on already available light sources designed for other applications. This paper is the first one to report the complex refractive index of the sciatic nerve of Xenopus laevis, which is a crucial parameter for identifying the optimal wavelength of optical stimuli. The Xenopus laevis neural tissue is the most widely used tissue type in peripheral neurostimulation studies. In this paper, the reflectance ( ${R}$ ) and the transmittance ( ${T}$ ) of the sciatic nerve were measured over a wavelength range of 860–2250 nm, and the corresponding real ( ${n}$ ) and the imaginary ( ${k}$ ) refractive indices were calculated using appropriate formulae in a novel way. The reported ${n}$ values were between 1.3–1.44 and the ${k}$ values are of the order of $textsf {10}^{-textsf {5}}$ over the full wavelength range. The absorption coefficient $alpha $ was found to be 100–500 cm $^{-1}$ . Several localized wavelength ranges were identified that can offer a maximized power coupling between potential opti- al stimuli and the neural tissue (1150–1200 nm, 1500–1700 nm, and 1900–2050 nm). The narrower regions of 1400–1600 nm and 1850–2150 nm were found to exhibit maximized absorbance. Separately, three regions were identified, where the penetration depths are the greatest (950–1000 nm, 1050–1350 nm, and 1600–1900 nm). This paper provides, for the first time, the fundamental specifications for optimizing the parameters of optical neurostimulation systems.

Technology-Assisted Ankle Rehabilitation Improves Balance and Gait Performance in Stroke Survivors: A Randomized Controlled Study With 1-Month Follow-Up

Fri, 11/30/2018 - 23:00
Many stroke survivors have limited ankle range of motion (ROM) caused by weak dorsiflexors and stiff plantarflexors. Passive ankle stretching exercises with physical therapists or a stretching board are usually recommended, but these treatments have some limitations (e.g., cost and availability of physical therapists). In this paper, we assessed the results of ankle stretching exercises delivered by a robotic ankle stretching system called motorized ankle stretcher (MAS) that we developed or by a stretching board on ankle ROM, balance control, and gait performance. The 16 stroke survivors were randomly assigned to an intervention group (IG) or a control group (CG) and participated in seven sessions of dorsiflexion stretching exercises for three-and-a-half consecutive weeks. Laboratory assessments included pre-assessment (baseline at the beginning of the first exercise session), post-assessment (at the end of the seventh exercise session), and retention assessment (one month after the seventh exercise session). All assessments included ankle ROM for the affected side, static/dynamic balance control with a sensory organization test (SOT), walking speed, walking cadence, and step length for the affected and unaffected sides. During seven sessions of ankle stretching exercises, the IG performed them using the MAS, and the CG used a stretching board. The IG significantly improved ankle ROM, SOT scores (i.e., static/dynamic balance control), walking speeds, walking cadences, and step lengths for the unaffected side after completing the seven exercise sessions of ankle stretching exercises and maintained the enhancements at the retention assessment. The CG did not significantly improve across the majority of outcome measures except for the SOT scores between the pre-assessment and retention assessment. Future work will investigate the ideal intensity, frequency, and duration of exercising with the MAS. Our research on technology-assisted ankle rehabilitation, which can as- ertain the level of persistent improvement, long-term performance retention, and carry-over effects in stroke survivors, can be used to inform future designs.

Instrumental Assessment of Stair Ascent in People With Multiple Sclerosis, Stroke, and Parkinson’s Disease: A Wearable-Sensor-Based Approach

Fri, 11/30/2018 - 23:00
Stair ascent is a challenging daily-life activity highly related to independence. This task is usually assessed with clinical scales suffering from partial subjectivity and limited detail in evaluating different task aspects. In this paper, we instrumented the assessment of stair ascent in people with multiple sclerosis (MS), stroke (ST), and Parkinson’s disease (PD) to analyze the validity of the proposed quantitative indexes and characterize subjects’ performances. Participants climbed 10 steps wearing a magneto-inertial sensor [magneto-inertial measurement unit (MIMU)] at sternum level. Gait pattern features (step frequency, symmetry, regularity, and harmonic ratios), and upper trunk sway were computed from MIMU signals. Clinical modified dynamic gait index (mDGI) and mDGI-Item 8 “Up stairs” were administered. Significant correlations with clinical scores were found for gait pattern features ( $text {r}_{text {s}} ge {0.536}$ ) and trunk pitch sway ( $text {r}_{textsf {s}} le -{0.367}$ ) demonstrating their validity. Instrumental indexes showed alterations in the three pathological groups compared to healthy subjects and significant differences, not clinically detected, among MS, ST, and PD. MS showed the worst performance, with alterations of all gait pattern aspects and larger trunk pitch sway. ST showed worsening in gait pattern features but not in trunk motion. PD showed fewer alterations consisting in reduced step frequency and trunk yaw sway. These results suggest that the use of an MIMU provided valid objective indexes revealing between-group differences in stair ascent not detected by clinical scales. Importantly, the indexes include upper trunk measures, usually not present in clinical tests, and provide relevant hints for tailored r- habilitation.

Restoring Natural Forearm Rotation in Transradial Osseointegrated Amputees

Fri, 11/30/2018 - 23:00
Osseointegrated transradial prostheses have the potential to preserve the natural range of wrist rotation, which improves the performance of activities of daily living and reduces compensatory movements that potentially lead to secondary health problems over time. This is possible by enabling the radius and the ulna bone to move with respect to each other, restoring the functionality of the original distal-radioulnar joint. In this paper, we report on psychophysics tests performed on an osseointegrated transradial amputee with the aim to understand the extent of mobility of the implants that is required to preserve the natural forearm rotation. Based on these experiments, we designed and developed an attachment device between the implants and the hand prosthesis that serves as an artificial distal radio-ulnar joint. This device was fitted on an osseointegrated transradial amputee and its functionality assessed by means of the Southampton Hand Assessment Procedure (SHAP) and the Minnesota Manual Dexterity test (MMDT). We found that the axial rotation of the implants is required to preserve forearm rotation, to distribute loads equally over the two implants (60% radius – 40% ulna), and to enable loading of the implants without unpleasant feelings for the patient. Higher function was recorded when our attachment device enabled forearm rotation: SHAP from 61 to 71, MMDT from 258s to 231s. Natural forearm rotation can be successfully restored in transradial amputees by using osseointegration and our novel mechanical attachment to the hand prosthesis.

Modeling the Kinematics of Human Locomotion Over Continuously Varying Speeds and Inclines

Fri, 11/30/2018 - 23:00
Powered knee and ankle prostheses can perform a limited number of discrete ambulation tasks. This is largely due to their control architecture, which uses a finite-state machine to select among a set of task-specific controllers. A non-switching controller that supports a continuum of tasks is expected to better facilitate normative biomechanics. This paper introduces a predictive model that represents gait kinematics as a continuous function of gait cycle percentage, speed, and incline. The basis model consists of two parts: basis functions that produce kinematic trajectories over the gait cycle and task functions that smoothly alter the weight of basis functions in response to task. Kinematic data from 10 able-bodied subjects walking at 27 combinations of speed and incline generate training and validation data for this data-driven model. Convex optimization accurately fits the model to experimental data. Automated model order reduction improves predictive abilities by capturing only the most important kinematic changes due to walking tasks. Constraints on a range of motion and jerk ensure the safety and comfort of the user. This model produces a smooth continuum of trajectories over task, an impossibility for finite-state control algorithms. Random sub-sampling validation indicates that basis modeling predicts untrained kinematics more accurately than linear interpolation.

Design and Validation of a Semi-Active Variable Stiffness Foot Prosthesis

Fri, 11/30/2018 - 23:00
This paper presents the design and validation of a novel lower limb prosthesis called the variable stiffness foot (VSF), designed to vary its forefoot stiffness in response to user activity. The VSF is designed as a semi-active device that adjusts its stiffness once per stride during swing phases, in order to minimize size, mass, and power consumption. The forefoot keel is designed as an overhung composite beam, whose stiffness is varied by moving a support fulcrum to change the length of the overhang. Stiffness modulation is programmed in response to the gait characteristics detected through foot trajectory reconstruction based on an embedded inertial sensor. The prototype VSF has a mass of only 649 g including the battery, and a build height of 87 mm. Mechanical testing demonstrated a forefoot stiffness range of 10–32 N/mm for the prototype, a threefold range of stiffness variation. The stiffness range can be altered by changing the keel material or geometry. Actuation testing showed that the VSF can make a full-scale stiffness adjustment within three strides, and tracks moderate speed-driven variations within one swing phase. Human subjects testing demonstrated greater energy storage and return with lower stiffness settings. This capability may be useful for the modulating prosthesis energy return to better mimic human ankle function. Subjective feedback indicated clear perception by the subjects of contrasts among the stiffness settings, including interpretation of scenarios for which different settings may be beneficial. Future applications of the VSF include adapting stiffness to optimize stairs, ramps, turns, and standing.

Conceptual Design of a Fully Passive Transfemoral Prosthesis to Facilitate Energy-Efficient Gait

Fri, 11/30/2018 - 23:00
In this paper, we present the working principle and conceptual design toward the realization of a fully-passive transfemoral prosthesis that mimics the energetics of the natural human gait. The fundamental property of the conceptual design consists of realizing an energetic coupling between the knee and ankle joints of the mechanism. Simulation results show that the power flow of the working principle is comparable with that in human gait and a considerable amount of energy is delivered to the ankle joint for the push-off generation. An initial prototype in half scale is realized to validate the working principle. The construction of the prototype is explained together with the test setup that has been built for the evaluation. Finally, experimental results of the prosthesis prototype during walking on a treadmill show the validity of the working principle.

An Asynchronous Control Paradigm Based on Sequential Motor Imagery and Its Application in Wheelchair Navigation

Fri, 11/30/2018 - 23:00
In this paper, an asynchronous control paradigm based on sequential motor imagery (sMI) is proposed to enrich the control commands of a motor imagery -based brain-computer interface. We test the feasibility and report the performance of this paradigm in wheelchair navigation control. By sequentially imaging left- and right-hand movements, the subjects can complete four sMI tasks in an asynchronous mode that are then encoded to control six steering functions of a wheelchair, including moving forward, turning left, turning right, accelerating, decelerating, and stopping. Two experiments, a simulated experiment, and an online wheelchair navigation experiment, were conducted to evaluate the performance of the proposed approach in seven subjects. In summary, the subjects completed 99 of 105 experimental trials along a predefined route. The success rate was 94.2% indicating the practicality and the effectiveness of the proposed asynchronous control paradigm in wheelchair navigation control.

Design and Development of a Portable Exoskeleton for Hand Rehabilitation

Fri, 11/30/2018 - 23:00
Improvement in hand function to promote functional recovery is one of the major goals of stroke rehabilitation. This paper introduces a newly developed exoskeleton for hand rehabilitation with a user-centered design concept, which integrates the requirements of practical use, mechanical structure, and control system. The paper also evaluated the function with two prototypes in a local hospital. Results of functional evaluation showed that significant improvements were found in ARAT (P = 0.014), WMFT (P = 0.020), and FMA_WH (P = 0.021). Increase in the mean values of FMA_SE was observed but without significant difference (P = 0.071). The improvement in ARAT score reflects the motor recovery in hand and finger functions. The increased FMA scores suggest there is a motor improvement in the whole upper limb, and especially in the hand after the training. The product met patients’ requirements and has practical significance. It is portable, cost-effective, easy to use and supports multiple control modes to adapt to different rehabilitation phases.

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

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