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IEEE Transactions on Neural Systems and Rehabilitation Engineering

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https://www.readbyqxmd.com/read/28920904/modeling-the-nonlinear-cortical-response-in-eeg-evoked-by-wrist-joint-manipulation
#1
Martijn P Vlaar, Georgios Birpoutsoukis, John Lataire, Maarten Schoukens, Alfred C Schouten, Johan Schoukens, Frans C T van der Helm
Joint manipulation elicits a response from the sensors in the periphery which, via the spinal cord, arrives in the cortex. The average evoked cortical response recorded using electroencephalography was shown to be highly nonlinear; a linear model can only explain 10% of the variance of the evoked response, and over 80% of the response is generated by nonlinear behavior. The goal of this study is to obtain a nonparametric nonlinear dynamic model, which can consistently explain the recorded cortical response requiring little a priori assumptions about model structure...
September 13, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
https://www.readbyqxmd.com/read/28920903/brain-control-of-an-external-device-by-extracting-the-highest-force-related-contents-of-local-field-potentials-in-freely-moving-rats
#2
Abed Khorasani, Reza Foodeh, Vahid Shalchyan, Mohammad Reza Daliri
A local field potential (LFP) signal is an alternative source to neural action potentials for decoding kinematic and kinetic information from the brain. Here we demonstrate that better extraction of force-related features from multichannel LFPs improves the accuracy of force decoding. We propose that applying canonical correlation analysis (CCA) filter on the envelopes of separate frequency bands (band-specific CCA) seperates non- task related information from the LFPs. The decoding accuracy of the continuous force signal based on the proposed method were compared with three feature reduction methods: 1) Band-specific principal component analysis (bandspecific PCA) method that extract the components which leads to maximum variance from envelopes of different frequency bands, 2) Correlation coefficient-based (CC-based) feature reduction that selects the best features from the envelopes sorted based on the absolute correlation coefficient between each envelope and the target force signal and 3) Mutual information -based (MI-based) feature reduction that selects the best features from the envelopes sorted based on the mutual information between each envelope and output force signal...
September 12, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
https://www.readbyqxmd.com/read/28885156/the-vspa-foot-a-quasi-passive-ankle-foot-prosthesis-with-continuously-variable-stiffness
#3
Max K Shepherd, Elliott J Rouse
Most commercially available prosthetic feet do not exhibit a biomimetic torque-angle relationship, and are unable to modulate their mechanics to assist with other mobility tasks, such as stairs and ramps. In this work, we present a quasi-passive ankle-foot prosthesis with a customizable torque-angle curve and an ability to quickly modulate ankle stiffness between tasks. The customizable torque-angle curve is obtained with a cam-based transmission and a fiberglass leaf spring. To achieve variable stiffness, the leaf spring's support conditions can be actively modulated by a small motor, shifting the torque-angle curve to be more or less stiff...
September 7, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
https://www.readbyqxmd.com/read/28885155/a-nonlinear-dynamics-based-estimator-for-functional-electrical-stimulation-preliminary-results-from-lower-leg-extension-experiments
#4
Marcus Allen, Qiang Zhong, Nicholas Kirsch, Ashwin Dani, William W Clark, Nitin Sharma
Miniature inertial measurement units (IMUs) are wearable sensors that measure limb segment or joint angles during dynamic movements. However, IMUs are generally prone to drift, external magnetic interference, and measurement noise. This paper presents a new class of nonlinear state estimation technique called state-dependent coefficient (SDC) estimation to accurately predict joint angles from IMU measurements. The SDC estimation method uses limb dynamics, instead of limb kinematics, to estimate the limb state...
September 7, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
https://www.readbyqxmd.com/read/28880185/3d-object-recognition-of-a-robotic-navigation-aid-for-the-visually-impaired
#5
Cang Ye, Xiangfei Qian
This paper presents a 3D object recognition method and its implementation on a Robotic Navigation Aid (RNA) to allow real-time detection of indoor structural objects for the navigation of a blind person. The method segments a point cloud into numerous planar patches and extracts their Inter-Plane Relationships (IPRs). Based on the existing IPRs of the object models, the method defines 6 High Level Features (HLFs) and determines the HLFs for each patch. A Gaussian-Mixture-Model-based plane classifier is then devised to classify each planar patch into one belonging to a particular object model...
September 1, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
https://www.readbyqxmd.com/read/28880184/seizure-classification-from-eeg-signals-using-transfer-learning-semi-supervised-learning-and-tsk-fuzzy-system
#6
Yizhang Jiang, Dongrui Wu, Zhaohong Deng, Pengjiang Qian, Jun Wang, Guanjin Wang, Fu-Lai Chung, Kup-Sze Choi, Shitong Wang
Recognition of epileptic seizures from offline EEG signals is very important in clinical diagnosis of epilepsy. Compared with manual labeling of EEG signals by doctors, machine learning approaches can be faster and more consistent. However, the classification accuracy is usually not satisfactory for two main reasons: the distributions of the data used for training and testing may be different, and the amount of training data may not be enough. Additionally, most machine learning approaches generate black-box models that are difficult to interpret...
September 1, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
https://www.readbyqxmd.com/read/28880183/gaussian-process-autoregression-for-simultaneous-proportional-multi-modal-prosthetic-control-with-natural-hand-kinematics
#7
Michele Xiloyannis, Constantinos Gavriel, Andreas A C Thomik, A Aldo Faisal
Matching the dexterity, versatility and robustness of the human hand is still an unachieved goal in bionics, robotics and neural engineering. A major limitation for hand prosthetics lies in the challenges of reliably decoding user intention from muscle signals when controlling complex robotic hands. Most of the commercially available prosthetic hands use musclerelated signals to decode a finite number of predefined motions and some offer proportional control of open/close movements of the whole hand. Here, in contrast, we aim to offer users flexible control of individual joints of their artificial hand...
August 31, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
https://www.readbyqxmd.com/read/28866595/a-possible-explanation-of-how-high-frequency-deep-brain-stimulation-suppresses-low-frequency-tremors-in-parkinson-s-disease
#8
Vrutangkumar V V Shah, Sachin Goyal, Harish J Palanthandalam-Madapusi
Parkinson's disease (PD) is a neurodegenerative disorder of the central nervous system and one of its key symptoms is rest tremor. Deep Brain Stimulation (DBS) effectively suppresses rest tremor in Parkinson's disease. Despite being a successful treatment option, its underlying principle and the mechanism by which it attenuates tremors is not yet fully understood. Since existing methods for tuning DBS parameters are largely trial and error, understanding how DBS works can help reduce time and costs, and could also ultimately lead to better treatment strategies for PD...
August 29, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
https://www.readbyqxmd.com/read/28858808/using-inertial-sensors-to-automatically-detect-and-segment-activities-of-daily-living-in-people-with-parkinson-s-disease
#9
Hung Nguyen, Karina Lebel, Sarah Bogard, Etienne Goubault, Patrick Boissy, Qpn Clinicians, Christian Duval
Wearable sensors such as Inertial Measuring Units (IMUs) have been widely used to measure the quantity of physical activities during daily living in healthy and people with movement disorders through activity classification. These sensors have the potential to provide valuable information to evaluate the quality of the movement during activities of daily living (ADL) such as walking, sitting down and standing up, which could help clinicians to monitor rehabilitation and pharmaceutical interventions. However, high accuracy in the detection and segmentation of these activities is necessary for proper evaluation of the quality of the performance within a given segment...
August 25, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
https://www.readbyqxmd.com/read/28858807/bio-inspired-adaptive-control-for-active-knee-exoprosthetics
#10
Anna Pagel, Raffaele Ranzani, Robert Riener, Heike Vallery
On the quest to bring function of prosthetic legs closer to their biological counterparts, intuitive interplay of their control with the user's impedance modulation is key. We present two control features to enable more physiological and more user-adaptive control of prosthetic legs: a neuromusculoskeletal impedance model (NeurImp) including a reflexive component, and a human model reference adaptive controller (HuMRAC), which can be combined with the former. In stance-phase simulations, the NeurImp allowed to control a prosthetic leg with physiological knee joint angle and moment...
August 25, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
https://www.readbyqxmd.com/read/28858806/a-novel-nonlinear-dynamic-method-for-stroke-rehabilitation-effect-evaluation-using-eeg
#11
Hong Zeng, Guojun Dai, Wanzeng Kong, Fangyue Chen, Luyun Wang
Evaluating the effect of stroke rehabilitation based on electroencephalogram (EEG) is still a challenging problem. This paper presents a novel nonlinear dynamic complexity method for the evaluation of stroke rehabilitation effect from EEG signal. Our method calculates the nonlinearly separable degree (NLSD) of EEG signal, and then employs an indicator, called Mean Nonlinearly Separable complexity Degree (Mean_NLSD), to efficiently and accurately evaluate therapy effect of stroke patients. Our study under twelve stimuli conditions on eleven patients and eleven control subjects indicates that in general Mean_NLSD is smaller at the lesion regions and that the Mean_NLSD of the control subjects is stochastic...
August 25, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
https://www.readbyqxmd.com/read/28829312/feasibility-and-validity-of-discriminating-yaw-plane-head-on-trunk-motion-using-inertial-wearable-sensors
#12
Serene S Paul, Raymond G Walther, Ethan A Beseris, Leland E Dibble, Mark E Lester
A consequence of vestibular loss is increased coupling of head-on-trunk motion, particularly in the yaw plane, which adversely affects community mobility in these patients. Inertial sensors may provide a means of better understanding normal decoupling behaviors in community environments, but demonstration of their validity and responsiveness is needed. This study examined the validity and measurement sensitivity of inertial sensors in quantifying yaw plane head-trunk decoupling during unrestricted and restricted cervical motion conditions in healthy adults...
August 17, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
https://www.readbyqxmd.com/read/28816673/generating-arm-swing-trajectories-in-real-time-using-a-data-driven-model-for-gait-rehabilitation-with-self-selected-speed
#13
Babak Hejrati, Andrew S Merryweather, Jake J Abbott
Gait rehabilitation is often focused on the legs, and overlooks the role of the upper limbs. However, a variety of studies have demonstrated the importance of proper arm swing both during healthy walking and during rehabilitation. In this paper, we describe a method for generating proper arm-swing trajectories in real-time using only measurements of the angular velocity of a person's thighs, to be used during gait rehabilitation with self-selected walking speed. A datadriven linear time-invariant transfer function is developed, using frequency-response methods, which captures the frequencydependent magnitude and phase relationship between the thighs' angular velocities and the arm angles (measured at the shoulder, in the sagittal plane), using a data set of 30 healthy adult subjects...
August 14, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
https://www.readbyqxmd.com/read/28809705/effects-of-continuous-kinaesthetic-feedback-based-on-tendon-vibration-on-motor-imagery-bci-performance
#14
M Barsotti, D Leonardis, N Vanello, M Bergamasco, A Frisoli
BACKGROUND AND OBJECTIVES: Feedback plays a crucial role for using Brain Computer Interface (BCI) systems. This study proposes the use of vibration-evoked kinaesthetic illusions as part of a novel multisensory feedback for a Motor Imagery (MI) based BCI and investigates its contributions in terms of BCI performance and electroencephalographic (EEG) correlates. METHODS: Sixteen subjects performed two different right arm MI-BCI sessions: with the visual feedback only and with both visual and vibration-evoked kinaesthetic feedback, conveyed by the stimulation of the biceps brachi tendon...
August 14, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
https://www.readbyqxmd.com/read/28809703/motion-based-rapid-serial-visual-presentation-for-gaze-independent-brain-computer-interfaces
#15
Dong-Ok Won, Han-Jeong Hwang, Dong-Min Kim, Klaus-Robert Muller, Seong-Whan Lee
Most event-related potential (ERP)-based braincomputer interface (BCI) spellers primarily use matrix layouts, and generally require moderate eye movement for successful operation. The fundamental objective of this study is to enhance the perceptibility of target characters by introducing motion stimuli to classical rapid serial visual presentation (RSVP) spellers that do not require any eye movement, thereby applying them to paralyzed patients with oculomotor dysfunctions. To test the feasibility of the proposed motion-based RSVP paradigm, we implemented three RSVP spellers: i) fixed-direction motion (FM-RSVP), ii) random-direction motion (RM-RSVP), and iii) (the conventional) non-motion stimulation (NM-RSVP), and evaluated the effect of the three different stimulation methods on spelling performance...
August 11, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
https://www.readbyqxmd.com/read/28809704/kilohertz-electrical-stimulation-nerve-conduction-block-effects-of-electrode-material
#16
Yogi A Patel, Brian S Kim, Robert J Butera
Kilohertz electrical stimulation (KES) has enabled a novel new paradigm for spinal cord and peripheral nerve stimulation to treat a variety of neurological diseases. KES can excite or inhibit nerve activity and is used in many clinical devices today. However, the impact of different electrode materials on the efficacy of KES is unknown. We investigated the effect of different electrode materials and their respective charge injection mechanisms on KES nerve block thresholds using 20 and 40 kHz current-controlled sinusoidal KES waveforms...
August 10, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
https://www.readbyqxmd.com/read/28792901/an-automated-classification-of-pathological-gait-using-unobtrusive-sensing-technology
#17
Elham Dolatabadi, Babak Taati, Alex Mihailidis
This study integrates an unobtrusive and affordable sensing technology with machine learning methods to discriminate between healthy and pathological gait patterns as a result of stroke or acquired brain injury (ABI). A feature analysis is used to identify the role of each body part in separating pathological patterns from healthy patterns. Gait features including the orientations of the hips and spine (trunk), shoulders and neck (upper limb), knees and ankles (lower limb), are calculated during walking based on Kinect skeletal tracking sequences...
August 7, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
https://www.readbyqxmd.com/read/28767373/mixed-neural-network-approach-for-temporal-sleep-stage-classification
#18
Hao Dong, Akara Supratak, Wei Pan, Chao Wu, Paul M Matthews, Yike Guo
This paper proposes a practical approach to addressing limitations posed by using of single-channel electroencephalography (EEG) for sleep stage classification. EEG-based characterizations of sleep stage progression contribute the diagnosis and monitoring of the many pathologies of sleep. Several prior reports explored ways of automating the analysis of sleep EEG and of reducing the complexity of the data needed for reliable discrimination of sleep stages at lower cost in the home. However, these reports have involved recordings from electrodes placed on the cranial vertex or occiput, which are both uncomfortable and difficult to position...
July 28, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
https://www.readbyqxmd.com/read/28767372/texture-classification-and-visualization-of-time-series-of-gait-dynamics-in-patients-with-neuro-degenerative-diseases
#19
Tuan D Pham
The analysis of gait dynamics is helpful for predicting and improving the quality of life, morbidity, and mortality in neuro-degenerative patients. Feature extraction of physiological time series and classification between gait patterns of healthy control subjects and patients are usually carried out on the basis of one-dimensional signal analysis. The proposed approach presented in this paper departs itself from conventional methods for gait analysis by transforming time series into images, of which texture features can be extracted from methods of texture analysis...
July 27, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
https://www.readbyqxmd.com/read/28742045/a-multi-class-tactile-brain-computer-interface-based-on-stimulus-induced-oscillatory-dynamics
#20
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
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