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IEEE Transactions on Neural Systems and Rehabilitation Engineering

Richard J Adams, Matthew D Lichter, Allison Ellington, Marga White, Kate Armstead, James T Patrie, Paul T Diamond
A study was conducted to investigate the effectiveness of virtual activities of daily living (ADL) practice using the SaeboVR software system for the recovery of upper extremity (UE) motor function following stroke. The system employs Kinect sensor-based tracking to translate human UE motion into the anatomical pose of the arm of the patient's avatar within a virtual environment, creating a virtual presence within a simulated task space. Patients gain mastery of 12 different integrated activities while traversing a metaphorical "road to recovery" that includes thematically linked levels and therapist-selected difficulty settings...
January 2018: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Tamas Kapelner, Francesco Negro, Oskar C Aszmann, Dario Farina
We prove the feasibility of decomposing high density surface EMG signals from forearm muscles in non-isometric wrist motor tasks of normally limbed and limb-deficient individuals with the perspective of using the decoded neural information for prosthesis control. For this purpose, we recorded surface EMG signals during motions of three degrees of freedom of the wrist in seven normally limbed subjects and two patients with limb deficiency. The signals were decomposed into individual motor unit activity with a convolutive blind source separation algorithm...
January 2018: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Lana Popovic-Maneski, Antonina Aleksic, Amine Metani, Vance Bergeron, Radoje Cobeljic, Dejan B Popovic
Increased muscle tone and exaggerated tendon reflexes characterize most of the individuals after a spinal cord injury (SCI). We estimated seven parameters from the pendulum test and used them to compare with the Ashworth modified scale of spasticity grades in three populations (retrospective study) to assess their spasticity. Three ASIA B SCI patients who exercised on a stationary FES bicycle formed group F, six ASIA B SCI patients who received only conventional therapy were in the group C, and six healthy individuals constituted the group H...
January 2018: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Venustiano Soancatl Aguilar, Jasper J van de Gronde, Claudine J C Lamoth, Natasha M Maurits, Jos B T M Roerdink
Improving balance performance among the elderly is of utmost importance because of the increasing number of injuries and fatalities caused by fall incidences. Digital games controlled by body movements (exergames) have been proposed as a way to improve balance among older people. However, the assessment of balance performance in real-time during exergaming remains a challenging task. This assessment could be used to provide instantaneous feedback and automatically adjust the exergame difficulty. Such features could potentially increase the motivation of the player, thus augmenting the effectiveness of exergames...
January 2018: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Anja Jackowski, Marion Gebhard, Roland Thietje
The assistive robot system adaptive head motion control for user-friendly support (AMiCUS) has been developed to increase the autonomy of motion impaired people. The six degrees of freedom robot arm with gripper is controlled with head motion and head gestures only, so especially tetraplegics benefit from collaboration with AMiCUS. In this paper, a usability study with a total number of 30 subjects was conducted to validate the AMiCUS interaction technology and design. 24 able-bodied subjects of demographically diverse groups and 6 tetraplegics participated in this paper...
January 2018: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Pejman Memar, Farhad Faradji
Sleep stage classification is one of the most critical steps in effective diagnosis and the treatment of sleep-related disorders. Visual inspection undertaken by sleep experts is a time-consuming and burdensome task. A computer-assisted sleep stage classification system is thus essential for both sleep-related disorders diagnosis and sleep monitoring. In this paper, we propose a system to classify the wake and sleep stages with high rates of sensitivity and specificity. The EEG signals of 25 subjects with suspected sleep-disordered breathing, and the EEG signals of 20 healthy subjects from three data sets are used...
January 2018: IEEE Transactions on Neural Systems and Rehabilitation Engineering
M Kerem Un, Hamed Kaghazchi
When a signal is initiated in the nerve, it is transmitted along each nerve fiber via an action potential (called single fiber action potential (SFAP)) which travels with a velocity that is related with the diameter of the fiber. The additive superposition of SFAPs constitutes the compound action potential (CAP) of the nerve. The fiber diameter distribution (FDD) in the nerve can be computed from the CAP data by solving an inverse problem. This is usually achieved by dividing the fibers into a finite number of diameter groups and solve a corresponding linear system to optimize FDD...
January 2018: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Ruben Cubo, Mattias Astrom, Alexander Medvedev
Deep brain stimulation (DBS) is a neurosurgical treatment in, e.g., Parkinson's Disease. Electrical stimulation in DBS is delivered to a certain target through electrodes implanted into the brain. Recent developments aiming at better stimulation target coverage and lesser side effects have led to an increase in the number of contacts in a DBS lead as well as higher hardware complexity. This paper proposes an optimization-based approach to alleviation of the fault impact on the resulting therapeutical effect in field steering DBS...
January 2018: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Lin Yao, Xinjun Sheng, Natalie Mrachacz-Kersting, Xiangyang Zhu, Dario Farina, Ning Jiang
A large proportion of users do not achieve adequate control using current non-invasive brain-computer interfaces (BCIs). This issue has being coined "BCI-Illiteracy" and is observed among different BCI modalities. Here, we compare the performance and the BCI-illiteracy rate of a tactile selective sensation (SS) and motor imagery (MI) BCI, for a large subject samples. We analyzed 80 experimental sessions from 57 subjects with two-class SS protocols. For SS, the group average performance was 79.8 ± 10.6%, with 43 out of the 57 subjects (75...
January 2018: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Hossein Zamani, Hamid Reza Bahrami, Preeti Chalwadi, Paul A Garris, Pedram Mohseni
This paper presents a novel compressive sensing framework for recording brain dopamine levels with fast-scan cyclic voltammetry (FSCV) at a carbon-fiber microelectrode. Termed compressive FSCV (C-FSCV), this approach compressively samples the measured total current in each FSCV scan and performs basic FSCV processing steps, e.g., background current averaging and subtraction, directly with compressed measurements. The resulting background-subtracted faradaic currents, which are shown to have a block-sparse representation in the discrete cosine transform domain, are next reconstructed from their compressively sampled counterparts with the block sparse Bayesian learning algorithm...
January 2018: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Yang Yu, Zongtan Zhou, Yadong Liu, Jun Jiang, Erwei Yin, Nannan Zhang, Zhihua Wang, Yaru Liu, Xingjie Wu, Dewen Hu
This paper presents a hybrid brain-computer interface (BCI) that combines motor imagery (MI) and P300 potential for the asynchronous operation of a brain-controlled wheelchair whose design is based on a Mecanum wheel. This paradigm is completely user-centric. By sequentially performing MI tasks or paying attention to P300 flashing, the user can use eleven functions to control the wheelchair: move forward/backward, move left/right, move left45/right45, accelerate/decelerate, turn left/right, and stop. The practicality and effectiveness of the proposed approach were validated in eight subjects, all of whom achieved good performance...
December 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Weibao Qiu, Juan Zhou, Yan Chen, Min Su, Guofeng Li, Huixia Zhao, Xianyi Gu, De Meng, Congzhi Wang, Yang Xiao, Kwok Ho Lam, Jiyan Dai, Hairong Zheng
Fundamental insights into the function of the neural circuits often follows from the advances in methodologies and tools for neuroscience. Electrode- and optical- based stimulation methods have been used widely for neuro-modulation with high resolution. However, they are suffering from inherent invasive surgical procedure. Ultrasound has been proved as a promising technology for neuro-stimulation in a non-invasive manner. However, no portable ultrasound system has been developed particularly for neuro-stimulation...
December 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Sharon Israely, Gerry Leisman, Chay Machluf, Tal Shnitzer, Eli Carmeli
Functional tasks of the upper extremity can be executed by a variety of muscular patterns, independent of the direction, speed and load of the task. This large number of degrees of freedom imposes a significant control burden on the CNS. Previous studies suggested that the human cortex synchronizes a discrete number of neural functional units within the brainstem and spinal cord, i.e. muscle synergies, by linearly combining them to execute a great repertoire of movements. Further exploring this control mechanism, we aim to study whether a single set of muscle synergies might be generalized to express movements in different directions...
December 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
James Gardiner, Abu Zeeshan Bari, Laurence Kenney, Martin Twiste, David Moser, Saeed Zahedi, David Howard
Current energy storage and return prosthetic feet only marginally reduce the cost of amputee locomotion compared with basic solid ankle cushioned heel feet, possibly due to their lack of push-off at the end of stance. To the best of our knowledge, a prosthetic ankle that utilizes a hydraulic variable displacement actuator (VDA) to improve push-off performance has not previously been proposed. Therefore, here we report a design optimization and simulation feasibility study for a VDA-based prosthetic ankle. The proposed device stores the eccentric ankle work done from heel strike to maximum dorsiflexion in a hydraulic accumulator and then returns the stored energy to power push-off...
December 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Alycia S Gailey, Sasha Blue Godfrey, Ryan E Breighner, Karen L Andrews, Kristin D Zhao, Antonio Bicchi, Marco Santello
Current prosthetic hands are frequently rejected in part due to limited functionality and versatility. We assessed the feasibility of a novel prosthetic hand, the SoftHand Pro (SHP), whose design combines soft robotics and hand postural synergies. Able-bodied subjects ( ) tracked cursor motion by opening and closing the SHP and performed a grasp-lift-hold-release (GLHR) task with a sensorized cylindrical object of variable weight. The SHP control was driven by electromyographic (EMG) signals from two antagonistic muscles...
December 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Ji Chen, Diane Nichols, Elizabeth B Brokaw, Peter S Lum
In previous work, we developed a lightweight wearable hand exoskeleton (Hand Spring Operated Movement Enhancer) that improves range of motion and function in laboratory testing. In this pilot study, we added the ability to log movement data for extended periods and recruited ten chronic stroke subjects to use the device during reach and grasp task practice at home for 1.5 h/day, five days per week, and for four weeks. Seven subjects completed the study, performing 448 ± 651 hand movements per training day...
December 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Stefan L Sumsky, Marc H Schieber, Nitish V Thakor, Sridevi V Sarma, Sabato Santaniello
Neural decoders of kinematic variables have largely relied on task-dependent (TD) encoding models of the neural activity. TD decoders, though, require prior knowledge of the tasks, which may be unavailable, lack scalability as the number of tasks grows, and require a large number of trials per task to reduce the effects of neuronal variability. The execution of movements involves a sequence of phases (e.g., idle, planning, and so on) whose progression contributes to the neuronal variability. We hypothesize that information about the movement phase facilitates the decoding of kinematics and compensates for the lack of prior knowledge about the task...
November 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Marzieh Haghighi, Mohammad Moghadamfalahi, Murat Akcakaya, Barbara G Shinn-Cunningham, Deniz Erdogmus
Recent findings indicate that brain interfaces have the potential to enable attention-guided auditory scene analysis and manipulation in applications, such as hearing aids and augmented/virtual environments. Specifically, noninvasively acquired electroencephalography (EEG) signals have been demonstrated to carry some evidence regarding, which of multiple synchronous speech waveforms the subject attends to. In this paper, we demonstrate that: 1) using data- and model-driven cross-correlation features yield competitive binary auditory attention classification results with at most 20 s of EEG from 16 channels or even a single well-positioned channel; 2) a model calibrated using equal-energy speech waveforms competing for attention could perform well on estimating attention in closed-loop unbalanced-energy speech waveform situations, where the speech amplitudes are modulated by the estimated attention posterior probability distribution; 3) such a model would perform even better if it is corrected (linearly, in this instance) based on EEG evidence dependence on speech weights in the mixture; and 4) calibrating a model based on population EEG could result in acceptable performance for new individuals/users; therefore, EEG-based auditory attention classifiers may generalize across individuals, leading to reduced or eliminated calibration time and effort...
November 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Kurt A Kaczmarek, Mitchell E Tyler, Uchechukwu O Okpara, Steven J Haase
Sensations elicited by electrical stimulation of touch are multidimensional, varying in perceived intensity and quality in response to changes in stimulus current or waveform timing. This paper manipulated both current and frequency, while volunteer participants estimated the dissimilarity of all non-identical pairs of 16 stimulus conditions. Multidimensional scaling analysis revealed that a model having two perceptual dimensions was adequate in representing the electrotactile (electrocutaneous) sensations...
November 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Beom-Chan Lee, Alberto Fung, Timothy A Thrasher
A coding scheme for earlier versions of vibrotactile biofeedback systems for balance-related applications was primarily binary in nature, either off or on at a given threshold (range of postural tilt), making it unable to convey information about error magnitude. The purpose of this study was to explore the effects of two coding schemes (binary vs. continuous) for vibrotactile biofeedback during dynamic weight-shifting exercises that are common physical therapists' recommended balance exercises used in clinical settings...
October 16, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
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