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IEEE Transactions on Neural Systems and Rehabilitation Engineering

Lin Yao, Mei Lin Chen, Xinjun Sheng, Natalie Mrachacz-Kersting, Xiangyang Zhu, Dario Farina, Ning Jiang
We proposed a multi-class tactile brain-computer interface that utilizes stimulus-induced oscillatory dynamics. It was hypothesized that somatosensory attention can modulate tactile induced oscillation changes, which can decode different sensation attention tasks. Subjects performed four tactile attention tasks, prompted by cues presented in random order and while both wrists were simultaneously stimulated: 1) selective sensation on left hand (SS-L), 2) selective sensation on right hand (SS-R), 3) bilateral selective sensation (SS-B), and 4) selective sensation suppressed or idle state (SS-S)...
July 24, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Calvin D Eiber, Socrates Dokos, Nigel H Lovell, Gregg J Suaning
Visual prostheses are now an available mobility aid for patients blinded by degenerative retinal diseases. However, the spatial resolution of existing devices is still insufficient to deliver normal levels of mobility vision without stimulation strategies which enable existing devices to deliver several different percepts per stimulation site. A stimulation strategy in which field shaping is achieved by incorporating multipolar (bipolar and tripolar) stimulation could convey additional information to a user of a visual prosthesis, as compared to monopolar stimulation, is investigated...
July 24, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Kyunghwan Choi, Pyungkang Kim, Kyung-Soo Kim, Soohyun Kim
One of the long-standing challenges in upper limb prosthetics is restoring the sensory feedback that is missing due to amputation. Two approaches have previously been presented to provide various types of sensory information to users, namely, multi-modality sensory feedback and using an array of singlemodality stimulators. However, the feedback systems used in these approaches were too bulky to be embedded in prosthesis sockets. In this paper, we propose an electrocutaneous sensory feedback method that is capable of conveying two modalities simultaneously with only one electrode...
July 24, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Christian Breitwieser, Michele Tavella, Martijn Schreuder, Febo Cincotti, Robert Leeb, Gernot R Muller-Putz
In this paper, we present and analyze an event distribution system for brain-computer interfaces (BCIs). Events are commonly used to mark and describe incidents during an experiment and are therefore critical for later data analysis or immediate real-time processing. The presented approach, called TiD (Tools for brain-computer interaction - interface D), delivers messages in XML format via a bus-like system using TCP (transmission control protocol) connections or shared memory. A dedicated server dispatches TiD messages to distributed or local clients...
July 18, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Rosario Sorbello, Salvatore Tramonte, Marcello Giardina, Vincenzo La Bella, Rossella Spataro, Brendan Allison, Christoph Guger, Antonio Chella
This paper illustrates a new architecture for a human-humanoid interaction based on EEG-Brain Computer Interface (EEG-BCI) for patients affected by locked-in syndrome caused by Amyotrophic Lateral Sclerosis (ALS). The proposed architecture is able to recognise users' mental state accordingly to the biofeedback factor Bf , based on users' Attention, Intention and Focus, that is used to elicit a robot to perform customised behaviours. Experiments have been conducted with a population of 8 subjects: 4 ALS patients in a near Locked-in status with normal ocular movement and 4 healthy control subjects enrolled for age, education and computer expertise...
July 18, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Kramay Patel, Matija Milosevic, Kimitaka Nakazawa, Milos R Popovic, Kei Masani
Trunk instability is a major problem for individuals with thoracic and cervical spinal cord injury. Functional electrical stimulation (FES) neuroprosthesis, a technology that uses small electrical currents to artificially contract muscles, has previously been utilized to improve trunk stability during quasistatic and dynamic sitting. The aim of this study was to develop the first powered wheelchair-based neuroprosthesis and to test its feasibility for improving trunk stability. Eleven male, able-bodied individuals participated in the feasibility study...
July 14, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Dheeraj Rathee, Haider Raza, Girijesh Prasad, Hubert Cecotti
The objective is to evaluate the impact of EEG referencing schemes and spherical surface Laplacian (SSL) methods on the classification performance of motor-imagery (MI) related brain-computer interface systems. Two EEG referencing schemes: common referencing, common average referencing (CAR) and three surface Laplacian methods: current source density (CSD), finite difference method, and SSL using realistic head model, were implemented separately for pre-processing of the EEG signals recorded at the scalp. A combination of filter bank common spatial filter for features extraction and support vector machine for classification was used for both pairwise binary classifications and four-class classification of MI tasks...
July 13, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Kiran K Karunakaran, Kevin M Abbruzzese, Hao Xu, Richard A Foulds
Human gait requires both haptic and visual feedback to generate and control rhythmic movements, and navigate environmental obstacles. Current lower extremity wearable exoskeletons that restore gait to individuals with paraplegia due to spinal cord injury rely completely on visual feedback to generate limited pre-programmed gait-variations, and generally provide little control by the user over the gait cycle. As an alternative to this limitation, we propose user control of gait in real time using healthy upper extremities...
July 13, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
David A Sanders Td Vr
Non-model-based control of a wheeled vehicle pulling two trailers is presented. It is a fun train for disabled children consisting of a locomotive and two carriages. The fun train has afforded opportunities for both disabled and able bodied young people to share an activity and has provided early driving experiences for disabled children; it has introduced them to assistive and powered mobility. The train is a nonlinear system and subject to nonholonomic kinematic constraints so that position and state depend on the path taken to get there...
July 12, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Virginie Attina, Faten Mina, Pierre Stahl, Yvan Duroc, Evelyne Veuillet, Eric Truy, Hung Thai-Van
Auditory Evoked Potentials (AEPs) are of great interest to objectively evaluate the audition in cochlear implant (CI) recipients. However, these measures are impeded by CI stimulation electrical artifacts present in the EEG. In a first part, this study investigates the use of a hybrid model approximating CI patient data. This model gives access to both uncontaminated and denoised data, thus allowing for the evaluation of CI artifact removal methods. Here the efficiency of Independent Component Analysis (ICA) is evaluated in the context of Auditory Steady State Responses (ASSRs)...
July 6, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Jaemin Lee, Minkyu Kim, Keehoon Kim
This paper proposes a novel control method to minimize muscle energy for power-assistant robotic systems that support the intended motions of a user under unknown external perturbations, using surface electromyogram (sEMG) signals. Conventional control methods based on force/torque (F/T) sensors have limitations to detect human intentions and could, presumably, misunderstand or distort such intentions because of external perturbations of the interaction forces, such as those found in activities of daily living (ADL)...
July 5, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Ameer Mohammed, Majid Zamani, Richard Bayford, Andreas Demosthenous
In Parkinson's disease (PD), on-demand deep brain stimulation (DBS) is required so that stimulation is regulated to reduce side effects resulting from continuous stimulation and PD exacerbation due to untimely stimulation. Also, the progressive nature of PD necessitates the use of dynamic detection schemes that can track the nonlinearities in PD. This paper proposes the use of dynamic feature extraction feature extraction and dynamic pattern classification to achieve dynamic PD detection taking into account the demand for high accuracy, low computation and real-time detection...
July 3, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Bethel A Osuagwu, Magdalena Zych, Aleksandra Vuckovic
Explicit motor imagery (eMI) is a widely used brain computer interface (BCI) paradigm, but not everybody can accomplish this task. Here we propose a BCI based on implicit motor imagery (iMI). We compared classification accuracy between eMI and iMI of hands. Fifteen able bodied people were asked to judge the laterality of hand images presented on a computer screen in a lateral or medial orientation. This judgement task is known to require mental rotation of a person's own hands which in turn is thought to involve iMI...
June 29, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Akara Supratak, Hao Dong, Chao Wu, Yike Guo
The present study proposes a deep learning model, named DeepSleepNet, for automatic sleep stage scoring based on raw single-channel EEG. Most of the existing methods rely on hand-engineered features which require prior knowledge of sleep analysis. Only a few of them encode the temporal information such as transition rules, which is important for identifying the next sleep stages, into the extracted features. In the proposed model, we utilize Convolutional Neural Networks to extract timeinvariant features, and bidirectional-Long Short-Term Memory to learn transition rules among sleep stages automatically from EEG epochs...
June 28, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Bor-Shing Lin, Pei-Chi Hsiao, Shu-Yu Yang, Che-Shih Su, I-Jung Lee
This study proposes a data glove system integrated with six-axis inertial measurement unit sensors for evaluating the hand function of patients who have suffered a stroke. The modular design of this data glove facilitates its use for stroke patients. The proposed system can use the hand's accelerations, angular velocities, and joint angles as calculated by a quaternion algorithm, to help physicians gain new insights into rehabilitation treatments. A clinical experiment was performed on 15 healthy subjects and 15 stroke patients whose Brunnstrom stages (BSs) ranged from 4 to 6...
June 27, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Benjamin Metcalfe, Chris Clarke, Nick Donaldson, John Taylor
Recordings made directly from the nervous system are a key tool in experimental electrophysiology and the development of bioelectronic medicines. Analysis of these recordings involves the identification of signals from individual neurons, a process known as spike sorting. A critical and limiting feature of spike sorting is the need to align individual spikes in time. However, electrophysiological recordings are made in extremely noisy environments that seriously limit the performance of the spike-alignment process...
June 16, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Shenghong He, Yuanqing Li
Electrooculography (EOG) signals, which can be used to infer the intentions of a user based on eye movements, are widely used in human-computer interface (HCI) systems. Most existing EOG-based HCI systems incorporate a limited number of commands because they generally associate different commands with a few different types of eye movements, such as looking up, down, left, or right. This paper presents a novel single-channel EOG-based HCI that allows users to spell asynchronously by only blinking. Forty buttons corresponding to 40 characters displayed to the user via a graphical user interface (GUI) are intensified in a random order...
June 15, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Hyung Seok Nam, Sukgyu Koh, Yoon Jae Kim, Jaewon Beom, Woo Hyung Lee, Shi-Uk Lee, Sungwan Kim
Spasticity is an important factor in designing wearable and lightweight exoskeleton neurorehabilitation robots. The proposed study evaluates biomechanical reactions of an exoskeleton robot to spasticity and establishes relevant guidelines for robot design. A two-axis exoskeleton robot is used to evaluate a group of 20 patients post-stroke with spastic elbow and/or wrist joints. All subjects are given isokinetic movements at various angular velocities within the capable range of motion for both joints. The resistance torque and corresponding angular position at each joint are recorded continuously...
June 9, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Marzieh Haghighi, Mohammad Moghadamfalahi, Murat Akcakaya, 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 seconds 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 unbalancedenergy 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 dependency on speech weights in the mixture; (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...
June 6, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Shivakeshavan Ratnadurai-Giridharan, Chung Cheung, Leonid Rubchinsky
Conventional deep brain stimulation (DBS) of basal ganglia uses high-frequency regular electrical pulses to treat Parkinsonian motor symptoms and has a series of limitations. Relatively new and not yet clinically tested optogenetic stimulation is an effective experimental stimulation technique to affect pathological network dynamics. We compared the effects of electrical and optogenetic stimulation of the basal ganglia on the pathological parkinsonian rhythmic neural activity. We studied the network response to electrical stimulation and excitatory and inhibitory optogenetic stimulations...
June 6, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
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