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IEEE Transactions on Neural Systems and Rehabilitation Engineering

Hao-Teng Hsu, Wai-Keung Lee, Kuo-Kai Shyu, Ting-Kuang Yeh, Chun-Yen Chang, Po-Lei Lee
Neural oscillatory activities existing in multiple fre-quency bands usually represent different levels of neurophysiolog-ical meanings, from micro-scale to macro-scale organizations. In this study, we adopted Holo-Hilbert spectral analysis (HHSA) to study the amplitude-modulated (AM) and frequency-modulated (FM) components in sensorimotor Mu rhythm, induced by slow- and fast-rate repetitive movements. The HHSA-based approach is a two-layer empirical mode decomposition (EMD) architecture, which firstly decomposes the EEG signal into a series of frequency-modulated intrinsic mode functions (IMF) and then decomposes each frequency-modulated IMF into a set of amplitude-modulated IMFs...
July 13, 2018: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Mayra Bittencourt-Villalpando, Natasha M Maurits
A brain-computer interface (BCI) is a system that allows communication between the central nervous system and an external device. The BCIs developed by various research groups differ in their main features and the comparison across studies is therefore challenging. Here, in the same group of 19 healthy participants, we investigate three different tasks (SSVEP, P300 and hybrid) that allowed four choices to the user without previous neurofeedback training. We used the same 64-channel EEG equipment to acquire data while participants performed each of the tasks...
July 13, 2018: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Aaron Tabor, Scott Bateman, Erik Scheme
While training is critical for ensuring initial success as well as continued adoption of a myoelectric powered prosthesis, relatively little is known about the amount of training that is necessary. In previous studies, participants have completed only a small number of sessions, leaving doubt about whether findings necessarily generalize to a longer-term clinical training program. Furthermore, a heavy emphasis has been placed on functional prosthesis use when assessing the effectiveness of myoelectric training...
July 12, 2018: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Yun Lu, Luzheng Bi, Jinling Lian, Hongqi Li
Brain-control behaviors (BCBs) are behaviors of humans that communicate with external devices by means of human brain rather than peripheral nerves or muscles. In this paper, to understand and simulate such behaviors, we propose a mathematical model by combining a queuing network-based encoding model with a brain-computer interface (BCI) model. Experimental results under the static tests show the effectiveness of the proposed model in simulating real BCBs. Furthermore, we verify the effectiveness and applicability of the proposed model through the dynamic experimental tests in a simulated vehicle...
July 12, 2018: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Dong Liu, Weihai Chen, Kyuhwa Lee, Ricardo Chavarriaga, Fumiaki Iwane, Mohamed Bouri, Zhongcai Pei, Jose Del R Millan
Brain-machine interfaces (BMIs) have been used to incorporate the user intention to trigger robotic devices by decoding movement onset from electroencephalography (EEG). Active neural participation is crucial to promote brain plasticity thus to enhance the opportunity of motor recovery. This study presents the decoding of lower-limb movement-related cortical potentials (MRCPs) with continuous classification and asynchronous detection.We executed experiments in a customized gait trainer where 10 healthy subjects performed self-initiated ankle plantar flexion...
July 11, 2018: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Erica M Rutter, Christopher L Langdale, James A Hokanson, Franz Hamilton, Hien Tran, Warren M Grill, Kevin B Flores
Bladder overactivity and incontinence and dysfunction can be mitigated by electrical stimulation of the pudendal nerve applied at the onset of a bladder contraction. Thus, it is important to predict accurately both bladder pressure and the onset of bladder contractions. We propose a novel method for prediction of bladder pressure using a time-dependent spectrogram representation of external urethral sphincter electromyographic activity and a least absolute shrinkage and selection operator regression model. There was a statistically significant improvement in prediction of bladder pressure compared to methods based on the firing rate of external urethral sphincter electromyographic activity...
July 9, 2018: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Dayi Bian, Zhaobo Zheng, Amy Swanson, Amy Weitlauf, Zachary Warren, Nilanjan Sarkar
Sensory processing differences, including auditory, visual, and tactile, are ideal targets for early detection of neurodevelopmental risks, such as autism spectrum disorder. However, most existing studies focus on the audiovisual paradigm and ignore the sense of touch. In this work, we present a multisensory delivery system that can deliver audio, visual, and tactile stimuli in a controlled manner and capture peripheral physiological, eye gaze and electroencephalogram response data. The novelty of the system is the ability to provide affective touch...
July 9, 2018: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Brandon N Fournier, Edward D Lemaire, Andrew J J Smith, Marc Doumit
Lower extremity powered exoskeletons (LEPEs) allow people with spinal cord injury (SCI) to stand and walk. However, the majority of LEPEs walk slowly and users can become fatigued from overuse of forearm crutches, suggesting LEPE design can be enhanced. Virtual prototyping is a cost-effective way of improving design; therefore, this research developed and validated two models that simulate walking with the Bionik Laboratories' ARKE exoskeleton attached to a human musculoskeletal model. The first model was driven by kinematic data from 30 able-bodied participants walking at realistic slow walking speeds (0...
July 9, 2018: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Steven Lessard, Pattawong Pansodtee, Ash Robbins, James M Trombadore, Sri Kurniawan, Mircea Teodorescu
For stroke survivors and many other people with upper-extremity impairment, daily life can be difficult without properly functioning arms. Some modern physical therapy exercises focus on rehabilitating people with these troubles by correcting patients' perceptions of their own body to eventually regain complete control and strength over their arms again. Augmentative wearable robots, such as upper-extremity exoskeletons and exosuits, may be able to assist this endeavor. A common drawback in many of these exoskeletons however is their inability to conform to the natural flexibility of the human body without a rigid base...
July 9, 2018: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Reza Sharif Razavian, Borna Ghannadi, Naser Mehrabi, Mark Charlet, John McPhee
Functional electrical stimulation (FES) can be used as a neuroprosthesis in which muscles are stimulated by electrical pulses to compensate for the loss of voluntary movement control. Modulating the stimulation intensities to reliably generate movements is a challenging control problem. This paper introduces a feedback controller for a multi-muscle FES system to control hand movements in a two-dimensional (table-top) task space. This feedback controller is based on a recent human motor control model, which uses muscle synergies to simplify its calculations and improve the performance...
July 5, 2018: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Chad G Rose, Evan Pezent, Claudia K Kann, Ashish D Deshpande, Marcia K O'Malley
Robotic devices have been proposed to meet the rising need for high intensity, long duration, and goal-oriented therapy required to regain motor function after neurological injury. Complementing this application, exoskeletons can augment traditional clinical assessments through precise, repeatable measurements of joint angles and movement quality. These measures assume that exoskeletons are making accurate joint measurements with a negligible effect on movement. For the coupled and coordinated joints of the wrist and hand, the validity of these two assumptions cannot be established by characterizing the device in isolation...
July 5, 2018: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Chen Liu, Changsong Zhou, Jiang Wang, Kenneth A Loparo
A mathematical modeling for description of oscillation suppression by deep brain stimulation (DBS) is explored in this work. High frequency DBS introduced to the basal ganglia network can suppress pathological neural oscillations that occur in the Parkinsonian state. However, selecting appropriate stimulation parameters remains a challenging issue due to the limited understanding of the underlying mechanisms of the Parkinsonian state and its control. In this work, we use describing function analysis to provide an intuitive way to select the optimal stimulation parameters based on a biologically plausible computational model of the Parkinsonian neural network...
July 5, 2018: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Andrew Haddock, Kyle T Mitchell, Andrew Miller, Jill L Ostrem, Howard J Chizeck, Svjetlana Miocinovic
Deep brain stimulation (DBS) programming, the systematic selection of fixed electrical stimulation parameters that deliver maximal therapeutic benefit while limiting side effects, poses several challenges in the treatment of movement disorders. DBS programming requires expertise of trained neurologists or nurses who assess patient symptoms according to standardized clinical rating scales and use patient reports of DBS-related side effects to adjust stimulation parameters and optimize therapy. In this study, we describe and validate an automated software platform for DBS programming for tremor associated with Parkinson's disease (PD) and essential tremor (ET)...
July 2, 2018: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Jan Rusz, Jan Hlavnicka, Tereza Tykalova, Michal Novotny, Petr Dusek, Karel Sonka, Evzen Ruzicka
Although smartphone technology provides new opportunities for the recording of speech samples in everyday life, its ability to capture prodromal speech impairment in persons at high risk of developing Parkinsons disease (PD) has never been investigated. Speech data were acquired through a smartphone as well as a professional microphone with linear frequency response from 50 participants with rapid eye movement sleep behavior disorder that are at high risk of developing PD and related neurodegenerative disorders...
June 29, 2018: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Mohamed Aboseria, Francesco Clemente, Leonard F Engels, Christian Cipriani
In the case of a hand amputation, the affected can use myoelectric prostheses to substitute the missing limb and regain motor functionality. Unfortunately, these prostheses do not restore sensory feedback, thus users are forced to rely on vision to avoid object slippage. This is cognitively taxing, as it requires continuous attention to the task. Thus, providing functionally effective sensory feedback is pivotal to reduce the occurrence of slip events and reduce the users' cognitive burden. However, only a few studies investigated which kind of feedback is the most effective for this purpose, mostly using unrealistic experimental scenarios...
June 29, 2018: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Zhaohong Deng, Peng Xu, Lixiao Xie, Kup-Sze Choi, Shitong Wang
Intelligent recognition of electroencephalogram (EEG) signals is an important means to detect seizure. Traditional methods for recognizing epileptic EEG signals are usually based on two assumptions: 1) adequate training examples are available for model training, and 2) the training set and the test set are sampled from datasets with the same distribution. Since seizures occur sporadically, training examples of seizures could be limited. Besides, the training and test sets are usually not sampled from the same distribution for generic non-patient-specific recognition of EEG signals...
June 25, 2018: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Jennifer M Vojtech, Gabriel J Cler, Cara E Stepp
Surface electromyography (sEMG) is a promising computer access method for individuals with motor impairments. However, optimal sensor placement is a tedious task requiring trial-and-error by an expert, particularly when recording from facial musculature likely to be spared in individuals with neurological impairments. We sought to reduce sEMG sensor configuration complexity by using quantitative signal features extracted from a short calibration task to predict human-machine interface (HMI) performance. A cursor control system allowed individuals to activate specific sEMG-targeted muscles to control an onscreen cursor and navigate a target selection task...
June 20, 2018: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Lin Yao, Natalie Mrachacz-Kersting, Xinjun Sheng, Xiangyang Zhu, Dario Farina, Ning Jiang
In this study, we investigated the performance of a multi-class brain-computer interface (BCI). The BCI system is based on the concept of somatosensory attentional orientation (SAO), in which the user shifts and maintains somatosensory attention by imagining the sensation of tactile stimulation of a body part. At the beginning of every trial, a vibration stimulus (200 ms) informed the subjects to prepare for the task. Four SAO tasks were performed following randomly presented cues: SAO of the left hand (SAO-LF), SAO of the right hand (SAO-RT), bilateral SAO (SAO-BI), and SAO suppressed or idle state (SAO-ID)...
June 18, 2018: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Kathryn M Olesnavage, V Amos G Winter
This study presents a novel framework that quantitatively connects the mechanical design of a prosthetic foot to its anticipated biomechanical performance. The framework uses kinetic inputs (ground reaction forces and center of pressure) to predict kinematic outputs of the lower leg segment by knowing the geometry and stiffness of the foot. The error between the predicted and target kinematics is evaluated using a root-mean-square error function called the Lower Leg Trajectory Error (LLTE). Using physiological kinetic inputs and kinematic targets, three model foot architectures were optimized to minimize the LLTE...
June 18, 2018: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Brielle Jb Lee, Adam Williams, Pinhas Ben-Tzvi
This paper presents the design and control of the intelligent sensing and force-feedback exoskeleton robotic (iSAFER) glove to create a system capable of intelligent object grasping initiated by detection of the user's intentions through motion amplification. Using a combination of sensory feedback streams from the glove, the system has the ability to identify and prevent object slippage, as well as adapting grip geometry to the object properties. The slip detection algorithm provides updated inputs to the force controller to prevent an object from being dropped, while only requiring minimal input from a user who may have varying degrees of functionality in their injured hand...
June 18, 2018: IEEE Transactions on Neural Systems and Rehabilitation Engineering
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