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IEEE Transactions on Neural Systems and Rehabilitation Engineering

Lu Meng
More and more studies propose that high frequency brain signals are promising biomarkers of epileptogenic zone. In this study, our aim is to investigate the neuromagnetic changes and brain network topological alterations during an interictal period at high frequency ranges (80-1000Hz) between healthy controls and epileptic patients with Magnetoencephalography (MEG). We analyzed neuromagnetic activities with accumulated source imaging, and constructed brain network based on graph theory. Neuromagnetic activity changes and brain network alterations between two groups were analyzed in three frequency bands: ripple (80-250Hz), fast ripples (FRs, 250-500Hz), and very high frequency oscillations (VHFO, 500-1000Hz)...
February 12, 2019: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Vincenzo Catrambone, Alberto Greco, Giuseppe Averta, Matteo Bianchi, Gaetano Valenza, Enzo Pasquale Scilingo
Recent functional magnetic resonance imaging (fMRI) studies have identified specific neural patterns related to three different categories of movements: intransitive (i.e., meaningful gestures that do not include the use of objects), transitive (i.e., actions involving an object), and tool-mediated (i.e., actions involving a tool to interact with an object). However, fMRI intrinsically limits the exploitation of these results in a real scenario, such as a brain-machine interface (BMI). In this study, we propose a new approach to automatically predict intransitive, transitive, or tool-mediated movements of the upper limb using electroencephalography (EEG) spectra estimated during a motor planning phase...
February 11, 2019: IEEE Transactions on Neural Systems and Rehabilitation Engineering
J Vrba, R Janca, M Blaha, P Jezdik, A Belohlavkova, P Krsek, D Vrba
This paper aims to employ numerical simulations to assess the risk of cellular damage during the application of a novel paradigm of electrical stimulation mapping (ESM) used in neurosurgery. The core principle of the paradigm is the use of short, high-intensity and high-frequency stimulation pulses. We developed a complex numerical model and performed coupled electro-thermal transient simulations. The model was optimized by incorporating ESM electrodes' resistance obtained during multiple intraoperative measurements and validated by comparing them with the results of temperature distribution measurement acquired by thermal imaging...
February 8, 2019: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Masashi Sekiya, Sho Sakaino, Toshiaki Tsuji
This paper addresses a techique to estimate muscle activity from movement data. Statistical models, such as linear regression (LR) models and artificial neural networks (ANNs), are good candidate estimation techniques. Although an ANN has a high estimation capability, it is frequently in the clinical application that a very small amount of data leads to performance deterioration. Conversely, an LR model needs fewer data, while its generalization performance is limited. In this paper therefore, a muscle activity estimation method is proposed that uses a linear logistic regression model to improve the generalization performance...
February 8, 2019: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Xiaoqian Mao, Wei Li, Chengwei Lei, Jing Jin, Feng Duan, Sherry Chen
This paper presents a new brain robot interaction system by fusing human and machine intelligence to improve the real-time control performance. This system consists of a hybrid P300 and SSVEP mode conveying a human being's intention, and the machine intelligence combining a Fuzzy-logic-based image processing algorithm with multi-sensor fusion technology. A subject selects an object of interest via P300, and the classification algorithm transfers the corresponding parameters to Improved Fuzzy Color Extractor (IFCE) for object extraction...
February 4, 2019: IEEE Transactions on Neural Systems and Rehabilitation Engineering
N de la Oliva, J Del Valle, I Delgado-Martinez, M Mueller, T Stieglitz, Xavier Navarro
Neuroprostheses aimed to restore lost functions after a limb amputation are based on the interaction with the nervous system by means of neural interfaces. Among the different designs, intraneural electrodes implanted in peripheral nerves represent a good strategy to stimulate nerve fibers to send sensory feedback and to record nerve signals to control the prosthetic limb. However, intraneural electrodes, as any device implanted in the body, induce a foreign body reaction (FBR) that results in the tissue encapsulation of the device...
February 4, 2019: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Zohreh Salimi, Martin Ferguson-Pell
Wheelchair manoeuvring has received little attention in the literature despite its importance in mobility and performing activities of daily living and its role in developing secondary injuries for wheelchair users. The focus in this study was technology development with iterative and proof of concept testing: three versions of a wheelchair simulator that were designed and developed for simulating curvilinear wheelchair propulsion in Virtual Reality were tested for their validity and reliability. The wheelchair simulators comprise of a sophisticated wheelchair ergometer in an immersive Virtual Reality environment and are developed for manual wheelchair propulsion...
February 1, 2019: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Huy Phan, Fernando Andreotti, Navin Cooray, Oliver Y Chen, Maarten De Vos
Automatic sleep staging has been often treated as a simple classification problem that aims at determining the label of individual target polysomnography (PSG) epochs one at a time. In this work, we tackle the task as a sequence-to-sequence classification problem that receives a sequence of multiple epochs as input and classifies all of their labels at once. For this purpose, we propose a hierarchical recurrent neural network named SeqSleepNet1. At the epoch processing level, the network consists of a filterbank layer tailored to learn frequency-domain filters for preprocessing and an attention-based recurrent layer designed for short-term sequential modelling...
January 31, 2019: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Negin Hesam-Shariati, Terry Trinh, Angelica G Thompson-Butel, Christine T Shiner, Stephen J Redmond, Penelope A McNulty
Impaired motor control post-stroke is typically measured using clinical assessments employing categorical and subjective scoring. We investigated quantitative kinematic parameters of a complex movement with therapy in chronic stroke. Tri-axial accelerometry of the more-affected arm of 24 patients was recorded during early- (day 2-3) and late- (days 12-14) therapy, and for 13 patients at 6-month follow-up. Clinical assessments included classification of motor-function as low, moderate or high. Kinematic parameters were measured during Wii-baseball swings to assess the effect of time and the level of motor-function...
January 31, 2019: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Ulysse Cote-Allard, Cheikh Latyr Fall, Alexandre Drouin, Alexandre Campeau-Lecours, Clement Gosselin, Kyrre Glette, Francois Laviolette, Benoit Gosselin
In recent years, deep learning algorithms have become increasingly more prominent for their unparalleled ability to automatically learn discriminant features from large amounts of data. However, within the field of electromyographybased gesture recognition, deep learning algorithms are seldom employed as they require an unreasonable amount of effort from a single person, to generate tens of thousands of examples. This work's hypothesis is that general, informative features can be learned from the large amounts of data generated by aggregating the signals of multiple users, thus reducing the recording burden while enhancing gesture recognition...
January 31, 2019: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Fei Wang, Yanbin He, Jun Qu, Yuan Cao, Yalin Liu, Feng Li, Zhuliang Yu, Ronghao Yu, Yuanqing Li
The Coma Recovery Scale-Revised (CRS-R) behavioural scale is commonly used for the clinical evaluation of patients with disorders of consciousness (DOC). However, since DOC patients generally cannot supply stable and efficient behavioural responses to external stimulation, evaluation results based on behavioural scales are not sufficiently accurate. In this study, we proposed a novel brain-computer interface (BCI) based on three-dimensional (3D) stereo audiovisual stimuli to supplement object recognition evaluation in the CRS-R...
January 31, 2019: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Kuangen Zhang, Caihua Xiong, Wen Zhang, Haiyuan Liu, Daoyuan Lai, Yiming Rong, Chenglong Fu
This study aims to present a robust environmental features recognition system (EFRS) for lower limb prosthesis, which can assist the control of prosthesis by predicting locomotion modes of amputees and estimating environmental features in the following steps. A depth sensor and an inertial measurement unit (IMU) are combined to stabilize the point cloud of environments. Subsequently, the 2D point cloud is extracted from origin 3D point cloud and is classified through a neural network. Environmental features, including slope of road, width, and height of stair, were also estimated via the 2D point cloud...
January 25, 2019: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Baiying Lei, Xiaolu Liu, Shuang Liang, Wenlong Hang, Qiong Wang, Kup-Sze Choi, Jing Qin
Brain-computer interfaces (BCIs) based on motor imagery (MI) have been widely used to support the rehabilitation of motor functions of upper limbs rather than lower limbs. This is probably because it is more difficult to detect brain activities of lower limb MI. In order to reliably detect the brain activities of lower limbs to restore or improve the walking ability of the disabled, we propose a new paradigm of walking imagery (WI) in a virtual environment (VE) in order to elicit reliable brain activities and achieve a significant training effect...
January 25, 2019: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Minsu Song, Jonghyun Kim
Enhancing motor imagery (MI) results in amplified event-related desynchronization (ERD) and is important for MI-based rehabilitation and brain-computer interface (BCI) applications. Many attempts to enhance MI by providing visual guidance have been reported. We believe that the rubber hand illusion (RHI), which induces body ownership over an external object, can provide better guidance to enhance MI; thus, an RHI-based paradigm with motorized moving rubber hand was proposed. To validate the proposed MI enhancing paradigm, we conducted an experimental comparison among paradigms with twenty healthy subjects...
January 25, 2019: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Andreas Meinel, Henrich Kolkhorst, Michael Tangermann
Data-driven spatial filtering algorithms optimize scores such as the contrast between two conditions to extract oscillatory brain signal components. Most machine learning approaches for filter estimation, however, disregard within-trial temporal dynamics and are extremely sensitive to changes in training data and involved hyperparameters. This leads to highly variable solutions and impedes the selection of a suitable candidate for, e.g., neurotechnological applications. Fostering component introspection, we propose to embrace this variability by condensing the functional signatures of a large set of oscillatory components into homogeneous clusters, each representing specific within-trial envelope dynamics...
January 25, 2019: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Yongte Zheng, Zifan Jiang, An Ping, Fang Zhang, Junming Zhu, Yueming Wang, Wentao Zhu, Kedi Xu
Closed-loop electrical stimulation is emerging as a promising neural modulation therapy for refractory epilepsy. However, the efficacy of electrical stimulation is less than optimal and the mechanism of seizure control is still unclear. In this work, we evaluated the acute seizure control efficacy of multi-site closed-loop stimulation (MSCLS) in a rodent model with a custom designed closed-loop neurostimulator. A total of 18 rats were injected with kainic-acid (KA) in CA3 of the left hippocampus to induce acute temporal lobe seizures...
January 25, 2019: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Emanuela Formaggio, Silvia F Storti, Lucio Pastena, Massimo Melucci, Lucio Ricciardi, Fabio Faralli, Riccardo Gagliardi, Gloria Menegaz
Although the recent years have witnessed a growing interest in functional connectivity (FC) through brain sources, FC in extreme situations has not been completely elucidated. This study aimed at investigating whether the expertise acquired during deep-sea diving is reflected in FC in a group of professional divers (PDs) compared with a group of new divers (NDs) and how it could affect concentration and stress levels. The source of brain frequency rhythms, derived by electroencephalography (EEG) acquisition in a hyperbaric chamber, were extracted in different frequency bands and the corresponding FC was estimated in order to compare the two groups...
January 24, 2019: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Haohan Zhang, Biing-Chwen Chang, Young-Jae Rue, Sunil K Agrawal
Head-neck interfaces have the potential to command and control orientation tasks when the hand-wrist is not available for use as a joystick. We pose the question in this paper - How well can the head-neck be used to perform orientation tasks when compared to the hand-wrist? Anatomically, the motion of the head-neck is similar to that of the hand-wrist. We hypothesize that the head-neck motion can be as effective as the motion of the hand-wrist to control orientation tasks. A study was designed to characterize the ability of head-neck to command and control general orientation tasks...
January 23, 2019: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Carles Igual, Jorge Igual, Janne M Hahne, Lucas C Parra
In proportional myographic control one can control either position or velocity of movement. Here we propose to use adaptive auto-regressive filters, so as to gradually adjust between the two. We implemented this in an adaptive system with closed-loop feedback, where both the user and the machine simultaneously attempt to follow a cursor on a two-dimensional arena. We tested this on 15 able-bodied and three limb-deficient participants using an 8-channel myoelectric armband. The humanmachine pairs learn to perform smoother cursor movements with a larger range of motion when using the auto-regressive filters, as compared to our previous efforts with moving-average filters...
January 23, 2019: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Shuting Sun, Xiaowei Li, Jing Zhu, Ying Wang, Rong La, Xuemin Zhang, Liuqing Wei, Bin Hu
Existing studies have shown functional brain networks in patients with major depressive disorder (MDD) have abnormal network topology structure. But the methods to construct brain network still exist some issues to be solved. This study is to explore reliable and robust construction methods of functional brain network using different coupling methods and binarization approaches, based on high-density 128-channel resting state EEG recordings from 16 MDD patients and 16 normal controls (NC). It was found that the combination of imaginary part of coherence (ICoh) and Cluster-Span Threshold (CST) outperformed other methods...
January 23, 2019: IEEE Transactions on Neural Systems and Rehabilitation Engineering
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