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IEEE Transactions on Neural Systems and Rehabilitation Engineering

Anwesha Sengupta, Anirban Dasgupta, Aritra Chaudhuri, Anjith George, Aurobinda Routray, Rajlakshmi Guha
This paper proposes a scheme for assessing the alertness levels of an individual using simultaneous acquisition of multimodal physiological signals and fusing the information into a single metric for quantification of alertness. The system takes Electroencephalogram (EEG), high-speed image sequence and speech data as inputs. Certain parameters are computed from each of these measures as indicators of alertness and a metric by a fusion of the parameters for indicating alertness level of an individual at an instant is proposed...
February 20, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Sayyed Mostafa Mostafavi, Stephen H Scott, Sean P Dukelow, Parvin Mousavi
Robotic technologies can provide objective, reliable tools for assessing a broad range of sensory, motor and cognitive functions. However, as additional tasks are developed on these platforms, the time necessary to assess a patient increases. In this paper, we present a hierarchical task selection strategy for five tasks that form part of the battery of standard tests performed on the KINARM robotic system. The strategy is built using dependencies derived through three types of analyses: i) nonlinear hierarchical ordering theory is applied to determine the ordering of five tasks; ii) the parameters of all tasks are also ranked using non-linear hierarchical ordering theory; and iii) a modeling technique, fast orthogonal search (FOS), is applied to assess the predictive power of each robotic task for estimation of other task parameters...
February 16, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Sunny Verma, Deepesh Kumar, Animesh Kumawat, Anirban Dutta, Uttama Lahiri
Stroke patients usually suffer from asymmetric posture due to hemi-paresis that can result in reduced postural controllability leading to a balance deficit. This deficit increases the risk of falls, which often makes them dependent on caregivers for community ambulation, thus deteriorating their quality of life. Conventional balance training involves rehabilitation exercises performed under physiotherapist's supervision where the scarcity of trained professionals as well as the cost of clinic-based rehabilitation programs can deter stroke survivors from undergoing regular balance training...
February 9, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Meena AbdelMaseeh, Daniel Stashuk
OBJECTIVE: A new measure of neuromuscular transmission instability, motor unit potential (MUP) jitter, is introduced. MUP jitter can be estimated quickly using motor unit potential trains (MUPTs) extracted from electromyographic (EMG) signals acquired using conventional clinical equipment and needle EMG electrodiagnostic protocols. The primary motivation for developing MUP jitter is to avoid the technical demands associated with estimating jitter using conventional single fibre EMG techniques...
February 9, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Hwang-Jae Lee, Suhyun Lee, Won Hyuk Chang, Keehong Seo, Youngbo Shim, Byung-Ok Choi, Gyu-Ha Ryu, Yun-Hee Kim
The aims of this study were to investigate the effectiveness of a newly developed wearable hip assist robot that uses an active assist algorithm to improve gait function, muscle effort, and cardiopulmonary metabolic efficiency in elderly adults. Thirty elderly adults (15 males/15 females) participated in this study. The experimental protocol consisted of overground gait at comfortable speed under three different conditions: free gait without robot assistance, robot-assisted gait with zero torque (RAG-Z), and full robot-assisted gait (RAG)...
February 6, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Lior Haviv, Hagit Friedman, Uri Bierman, Itzhak Glass, Anton Plotkin, Aharon Weissbrod, Sagit Shushan, Vadim Bluvshtein, Elena Aidinoff, Noam Sobel, Amiram Catz
Individuals with cervical spinal cord lesions (SCL) typically depend on caregivers to manually assist in coughing by pressing against their abdominal wall. Coughing can also be assisted by functional electric stimulation (FES) applied to abdominal muscles via surface electrodes. Efficacy of FES, however, depends on precise temporal synchronization. The sniff-controller is a trigger that enables paralyzed individuals to precisely control external devices through alterations in nasal airflow. We hypothesized that FES self-triggering by sniffcontroller may allow for effective cough timing...
January 31, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Vladimir Joukov, Vincent Bonnet, Michelle Karg, Gentiane Venture, Dana Kulic
This work proposes a method to enable the use of non-intrusive, small, wearable, wireless sensors to estimate the pose of the lower body during gait and other periodic motions and to extract objective performance measures useful for physiotherapy. The Rhythmic Extended Kalman Filter (Rhythmic- EKF) algorithm is developed to estimate the pose, learn an individualized model of periodic movement over time, and use the learned model to improve pose estimation. The proposed approach learns a canonical dynamical system model of the movement during online observation, which is used to accurately model the acceleration during pose estimation...
January 26, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Tristan Vouga, Katie Zhuang, Jeremy Olivier, Mikhail Lebedev, Miguel Nicolelis, Mohamed Bouri, Hannes Bleuler
Recent advances in the field of brain-machine interfaces (BMIs) have demonstrated enormous potential to shape the future of rehabilitation and prosthetic devices. Here, a lower-limb exoskeleton controlled by the intracortical activity of an awake, behaving rhesus macaque is presented as a proof-of-concept for a locomotor BMI. A detailed description of the mechanical device, including its innovative features and first experimental results, is provided. During operation, BMI-decoded position and velocity are directly mapped onto the bipedal exoskeleton's motions, which then move the monkey's legs as the monkey remains physically passive...
January 26, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Elissa D Ledoux, Michael Goldfarb
This paper assesses the metabolic effort exerted by three transfemoral amputees when using a powered knee and ankle prosthesis for stair ascent, relative to ascending stairs with passive knee and ankle prostheses. The paper describes a controller that provides step-over stair ascent behavior reflective of healthy stair ascent biomechanics, and describes its implementation in a powered prosthesis prototype. Stair ascent experiments were performed with three unilateral transfemoral amputee subjects, comparing the oxygen consumption required to ascend stairs using the powered prosthesis (with a step-over gait), relative to using their daily-use energetically passive prostheses (with a step-to gait)...
January 20, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Zhaoyang Qiu, Brendan Z Allison, Jing Jin, Yu Zhang, Xingyu Wang, Wei Li, Andrzej Cichocki
: Abstract-Background: Motor imagery (MI) is a mental representation of motor behavior. MI-based brain computer interfaces (BCIs) can provide communication for the physically impaired. The performance of MI based BCI mainly depends on the subject's ability to self-modulate EEG signals. Proper training can help naive subjects learn to modulate brain activity proficiently. However, training subjects typically involves abstract motor tasks and is time-consuming. METHODS: To improve the performance of naive subjects during motor imagery, a novel paradigm was presented that would guide naive subjects to modulate brain activity effectively...
January 19, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Jozsef Laczko, Robert Arthur Scheidt, Lucia S Simo, Davide Piovesan
It is well-documented that neurological deficits after stroke can disrupt motor control processes that affect the smoothness of reaching movements. The smoothness of hand trajectories during multi-joint reaching depends on shoulder and elbow joint angular velocities and their successive derivatives as well as on the instantaneous arm configuration and its rate of change. Right-handed survivors of unilateral hemiparetic stroke and neurologically-intact control participants held the handle of a two-joint robot and made horizontal planar reaching movements...
January 16, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Fabrizio Patane, Stefano Rossi, Fausto Del Sette, Juri Taborri, Paolo Cappa
This paper presents the modular design and control of a novel compliant lower limb multi-joint exoskeleton for the rehabilitation of ankle knee mobility and locomotion of pediatric patients with neurological diseases, such as Cerebral Palsy (CP). The device consists of an untethered powered knee-ankle-foot orthosis (KAFO), addressed as WAKE-up (Wearable Ankle Knee Exoskeleton), characterized by a position control and capable of operating synchronously and synergistically with the human musculoskeletal system...
January 11, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Andrew Myrden, Tom Chau
Traditional brain-computer interfaces often exhibit unstable performance over time. It has recently been proposed that passive brain-computer interfaces may provide a way to complement and stabilize these traditional systems. In this study, we investigated the feasibility of a passive brain-computer interface that uses electroencephalography to monitor changes in mental state on a single-trial basis. We recorded cortical activity from 15 locations while 11 able-bodied adults completed a series of challenging mental tasks...
January 9, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Tianxiao Jiang, Tao Jiang, Taylor Wang, Shanshan Mei, Qingzhu Liu, Yunlin Li, Xiaofei Wang, Sujit Prabhu, Zhiyi Sha, Nuri F Ince
During awake brain surgeries, electrocorticogram (ECoG) was recorded using a high density electrode grid from the motor cortex of two subjects while they were asked to execute spontaneous hand extension and flexion. Firstly, we characterized the spatio-spectral patterns of high-density ECoG during the hand movements. In both subjects, we observed event related desynchronization (ERD) in low frequency band (LFB:8-32Hz) and event related synchronization (ERS) in high frequency band (HFB:60-200Hz) where HFB-ERS was more spatially localized and movement specific compared to LFB-ERD...
January 4, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Kai Keng Ang, Cuntai Guan
Advances in Brain-Computer Interface (BCI) technology have facilitated the detection of Motor Imagery (MI) from electroencephalography (EEG). First, we present three strategies of using BCI to detect MI from EEG: operant conditioning that employed a fixed model, machine learning that employed a subject-specific model computed from calibration, and adaptive strategy that continuously compute the subjectspecific model. Second, we review prevailing works that employed the operant conditioning and machine learning strategies...
December 30, 2016: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Sareh Saeedi, Ricardo Chavarriaga, Jose Del R Millan
Performance variation is one of the main challenges that BCIs are confronted with, when being used over extended periods of time. Shared control techniques could partially cope with such a problem. In this paper, we propose a taxonomy of shared control approaches used for BCIs and we review some of the recent studies at the light of these approaches. We posit that the level of assistance provided to the BCI user should be adjusted in real time in order to enhance BCI reliability over time. This approach has not been extensively studied in the recent literature on BCIs...
December 28, 2016: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Emmanuele Tidoni, Mohammad Abu-Alqumsan, Daniele Leonardis, Christoph Kapeller, Gabriele Fusco, Christoph Guger, Cristoph Hintermueller, Angelika Peer, Antonio Frisoli, Franco Tecchia, Massimo Bergamasco, Salvatore M Aglioti
The development of technological applications that allow people to control and embody external devices within social interaction settings represents a major goal for current and future brain-computer interface (BCI) systems.
December 23, 2016: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Jiayuan He, Xiangyang Zhu
Pattern recognition-based myoelectric control is greatly influenced by electrode shift, which is inevitable during prosthesis donning and doffing. This study used gray-level co-occurrence matrix (GLCM) to represent the spatial distribution among high density (HD) electrodes and improved its calculation based on the using condition of myoelectric system, proposing a new feature, iGLCM, to improve the robustness of the system. The effects of its two parameters, quantization level and input data, were first evaluated and it was found that improved discrete Fourier transform (iDFT) performed better than the other three (time-domain, autoregressive, root mean square) as the input data of iGLCM, and increasing quantization level did not significantly decrease the error rate of iGLCM when it was above 8...
December 22, 2016: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Ian Loram, Brian Bate, Peter Harding, Ryan Cunningham, Alison Loram
While individual muscle function is known, the sensory and motor value of muscles within the whole-body sensorimotor network is complicated. Specifically, the relationship between neck muscle action and distal muscle synergies is unknown. This work demonstrates a causal relationship between regulation of the neck muscles and global motor control. Studying violinists performing unskilled and skilled manual tasks, we provided ultrasound feedback of the neck muscles with instruction to minimize neck muscle change during task performance and observed the indirect effect on whole-body movement...
December 21, 2016: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Maryam Saeed, Amir A Khan, Awais M Kamboh
High-density, intracranial recordings from micro-electrode arrays need to undergo Spike Sorting in order to associate the recorded neuronal spikes to particular neurons. This involves spike detection, feature extraction, and classification. To reduce the data transmission and power requirements, on-chip real-time processing is becoming very popular. However, high computational resources are required for classifiers in on-chip spike-sorters, making scalability a great challenge. In this review paper, we analyze several popular classifiers to propose five new hardware architectures using the off-chip training with on-chip classification approach...
December 19, 2016: IEEE Transactions on Neural Systems and Rehabilitation Engineering
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