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EISATC-Fusion: Inception Self-Attention Temporal Convolutional Network Fusion for Motor Imagery EEG Decoding IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-27 Guangjin Liang, Dianguo Cao, Jinqiang Wang, Zhongcai Zhang, Yuqiang Wu
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Lower-limb exoskeletons appeal to both clinicians and older adults, especially for fall prevention and joint pain reduction IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-27 Michael Raitor, Sandra Ruggles, Scott L. Delp, C. Karen Liu, Steven H. Collins
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OPM-MEG Measuring Phase Synchronization on Source Time Series: Application in Rhythmic Median Nerve Stimulation IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-26 Yu-Yu Ma, Yang Gao, Huan-Qi Wu, Xiao-Yu Liang, Yong Li, Hao Lu, Chang-Zeng Liu, Xiao-Lin Ning
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TBEEG: A Two-Branch Manifold Domain Enhanced Transformer Algorithm for Learning EEG Decoding IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-25 Yanjun Qin, Wenqi Zhang, Xiaoming Tao
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Assessment of Sensorized Insoles in Balance and Gait in Individuals with Parkinson’s Disease IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-25 A. Pergolini, T. Bowman, T. Lencioni, A. Marzegan, M. Meloni, M.C. Carrozza, E. Trigili, N. Vitiello, D. Cattaneo, S. Crea
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Tracking the Immediate and Short-Term Effects of Continuous Theta Burst Stimulation on Dynamic Brain States IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-25 Chao Chen, Zhidong Guo, Weiwei Peng, Shengpei Wang, Shuang Qiu, Jing Zhang, Xiaogang Chen, Huiguang He
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Identification of neural and non-neural origins of joint hyper-resistance based on a novel neuromechanical model. IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-25 Willaert Jente, Desloovere Kaat, Anja Van Campenhout, Lena H. Ting, Friedl De Groote
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Dynamics of Center of Pressure Trajectory in Gait: Unilateral Transfemoral Amputees versus Non-disabled Individuals IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-22 Yufan He, Mingyu Hu, Abu Jor, Hiroaki Hobara, Fan Gao, Toshiki Kobayashi
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Enhancing SSVEP-BCI Performance under Fatigue State Using Dynamic Stopping Strategy IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-22 Yuheng Han, Yufeng Ke, Ruiyan Wang, Tao Wang, Dong Ming
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Novel Wearable Device for Mindful Sensorimotor Training: Integrating Motor Decoding and Somatosensory Stimulation for Neurorehabilitation IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-21 Mirka Buist, Shahrzad Damercheli, Jan Zbinden, Minh Tat Nhat Truong, Enzo Mastinu, Max Ortiz-Catalan
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Performance of the Action Observation-Based Brain-Computer Interface in Stroke Patients and Gaze Metrics Analysis IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-21 Xin Zhang, Lin He, Qiang Gao, Ning Jiang
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Automated diagnosis of major depressive disorder with multi-modal MRIs based on contrastive learning: a few-shot study IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-21 Tongtong Li, Yuhui Guo, Ziyang Zhao, Miao Chen, Qiang Lin, Xiping Hu, Zhijun Yao, Bin Hu
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MRI Compatible Lumbopelvic Movement Measurement System to Validate and Capture Task Performance during Neuroimaging IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-21 Ahyoung Song, Kerrigan Sunday, Sheri P. Silfies, Jennifer MC. Vendemia
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Improving Walking Energy Efficiency in Transtibial Amputees Through the Integration of a Low-Power Actuator in an ESAR Foot IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-20 Alessandro Mazzarini, Ilaria Fagioli, Huseyin Eken, Chiara Livolsi, Tommaso Ciapetti, Alessandro Maselli, Michele Piazzini, Claudio Macchi, Angelo Davalli, Emanuele Gruppioni, Emilio Trigili, Simona Crea, Nicola Vitiello
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Robust Decoding of Upper-limb Movement Direction under Cognitive Distraction with Invariant Patterns in Embedding Manifold IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-20 Bolin Peng, Luzheng Bi, Zhi Wang, Aberham Genetu Feleke, Weijie Fei
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Transcranial Ultrasound Stimulation Improves Memory Performance of Parkinsonian Mice IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-19 Zhe Zhao, Hui Ji, Jiamin Pei, Jiaqing Yan, Xiangjian Zhang, Yi Yuan, Mengyang Liu
Cognitive impairment is one of the most common non-motor symptoms of Parkinson’s disease (PD). Previous studies have demonstrated that low-intensity transcranial ultrasound stimulation can significantly suppress the motor symptoms of PD. However, whether ultrasound stimulation can improve cognitive ability in PD and the related neural oscillation mechanism remain unclear to date. To evaluate the effect
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Ultrasound Stimulation Attenuates CRS-Induced Depressive Behavior by Modulating Dopamine Release in the Prefrontal Cortex IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-19 Ling Wang, Sutong Wang, Weiyi Mo, Yaqing Li, Qing Yang, Yutao Tian, Chenguang Zheng, Jiajia Yang, Dong Ming
Depression is one of the most serious mental disorders affecting modern human life and is often caused by chronic stress. Dopamine system dysfunction is proposed to contribute to the pathophysiology of chronic stress, especially the ventral tegmental area (VTA) which mainly consists of dopaminergic neurons. Focused ultrasound stimulation (FUS) is a promising neuromodulation modality and multiple studies
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Within-Session Reliability of fNIRS in Robot-Assisted Upper-Limb Training IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-19 Yi-Chuan Jiang, Chen Zheng, Rui Ma, Yifeng Chen, Sheng Ge, Chenyang Sun, Jianjun Long, Peng Fang, Mingming Zhang
Functional near-infrared spectroscopy (fNIRS) seems opportune for neurofeedback in robot-assisted rehabilitation training due to its noninvasive, less physical restriction, and no electromagnetic disturbance. Previous research has proved the cross-session reliability of fNIRS responses to non-motor tasks (e.g., visual stimuli) and fine-motor tasks (e.g., finger tapping). However, it is still unknown
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A Review of Intelligent Walking Support Robots: Aiding Sit-to-Stand Transition and Walking IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-19 Yu Sun, Cong Xiao, Lipeng Chen, Lu Chen, Haojian Lu, Yue Wang, Yu Zheng, Zhengyou Zhang, Rong Xiong
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M2M-InvNet: Human Motor Cortex Mapping from Multi-Muscle Response Using TMS and Generative 3D Convolutional Network IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-19 Md Navid Akbar, Mathew Yarossi, Sumientra Rampersad, Kyle Lockwood, Aria Masoomi, Eugene Tunik, Dana Brooks, Deniz Erdoğmuş
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Deep Learning Model to Evaluate Sensorimotor System Ability in Patients With Dizziness for Postural Control IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-18 Ahnryul Choi, Euyhyun Park, Tae Hyong Kim, Seungheon Chae, Gi Jung Im, Joung Hwan Mun
Balanced posture without dizziness is achieved via harmonious coordination of visual, vestibular, and somatosensory systems. Specific frequency bands of center of pressure (COP) signals during quiet standing are closely related to the sensory inputs of the sensorimotor system. In this study, we proposed a deep learning-based novel protocol using the COP signal frequencies to estimate the equilibrium
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Semantics-Guided Hierarchical Feature Encoding Generative Adversarial Network for Visual Image Reconstruction From Brain Activity IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-18 Lu Meng, Chuanhao Yang
The utilization of deep learning techniques for decoding visual perception images from brain activity recorded by functional magnetic resonance imaging (fMRI) has garnered considerable attention in recent research. However, reconstructed images from previous studies still suffer from low quality or unreliability. Moreover, the complexity inherent to fMRI data, characterized by high dimensionality and
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Repetitive Transcranial Alternating Current Stimulation to Improve Working Memory: An EEG-fNIRS Study IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-18 Dalin Yang, Min-Kyoung Kang, Guanghao Huang, Adam T. Eggebrecht, Keum-Shik Hong
Transcranial electrical stimulation has demonstrated the potential to enhance cognitive functions such as working memory, learning capacity, and attentional allocation. Recently, it was shown that periodic stimulation within a specific duration could augment the human brain’s neuroplasticity. This study investigates the effects of repetitive transcranial alternating current stimulation (tACS; 1 mA
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A Robust Causal Brain Network Measure and Its Application on Ictal Electrocorticogram Analysis of Drug-resistant Epilepsy IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-18 Yalin Wang, Wentao Lin, Hong Peng, Ligang Zhou, Wei Chen, Bin Hu
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Physics-Informed Deep Learning for Muscle Force Prediction With Unlabeled sEMG Signals IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-14 Shuhao Ma, Jie Zhang, Chaoyang Shi, Pei Di, Ian D. Robertson, Zhi-Qiang Zhang
Computational biomechanical analysis plays a pivotal role in understanding and improving human movements and physical functions. Although physics-based modeling methods can interpret the dynamic interaction between the neural drive to muscle dynamics and joint kinematics, they suffer from high computational latency. In recent years, data-driven methods have emerged as a promising alternative due to
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Improving Walking Path Generation Through Biped Constraint in Indoor Navigation System for Visually Impaired Individuals IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-11 Qingquan Na, Hui Zhou, Hailei Yuan, Mengfan Gui, Hongjing Teng
This paper introduces a walking path generation method specifically developed for the Smart Cane, which is a RNA (Robotic Navigation Assistance Device) aimed at enhancing indoor navigation for visually impaired individuals. The proposed approach combines the utilization of a LIPM (Linear Inverse Pendulum Model) and LFPC (Linear Foot Placement Controller) motion primitives to generate walking paths
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Lower-Limb Myoelectric Calibration Postures for Transtibial Prostheses IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-07 Ryan R. Posh, Emmalynn C. Barry, James P. Schmiedeler, Patrick M. Wensing
The use of an agonist-antagonist muscle pair for myoelectric control of a transtibial prosthesis requires normalizing the myoelectric signals and identifying their co-contraction signature. Extensive literature has explored the relationship between body posture and lower-limb muscle activation level using surface electromyography (EMG), but it is unknown how these relationships hold after amputation
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Brain Network Evaluation by Functional-Guided Effective Connectivity Reinforcement Learning Method Indicates Therapeutic Effect for Tinnitus IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-07 Han Lv, Jinduo Liu, Qian Chen, Junzhong Ji, Jihao Zhai, Zuozhen Zhang, Zhaodi Wang, Shusheng Gong, Zhenchang Wang
Using functional connectivity (FC) or effective connectivity (EC) alone cannot effectively delineate brain networks based on functional magnetic resonance imaging (fMRI) data, limiting the understanding of the mechanism of tinnitus and its treatment. Investigating brain FC is a foundational step in exploring EC. This study proposed a functionally guided EC (FGEC) method based on reinforcement learning
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EEG-Based Brain Functional Network Analysis for Differential Identification of Dementia-Related Disorders and Their Onset IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-07 Abdulyekeen T. Adebisi, Ho-Won Lee, Kalyana C. Veluvolu
Diagnosing and treating dementia, including mild cognitive impairment (MCI), is challenging due to diverse disease types and overlapping symptoms. Early MCI detection is vital as it can precede dementia, yet distinguishing it from later stage dementia is intricate due to subtle symptoms. The primary objective of this study is to adopt a complex network perspective to unravel the underlying pathophysiological
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Amplitude Adaptive Modulation of Neural Oscillations Over Long-Term Dynamic Conditions: A Computational Study IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-06 Zhaoyu Quan, Yan Li, Xi Cheng, Yingnan Nie, Shouyan Wang
Closed-loop deep brain stimulation (DBS) shows great potential for precise neuromodulation of various neurological disorders, particularly Parkinson’s disease (PD). However, substantial challenges remain in clinical translation due to the complex programming procedure of closed-loop DBS parameters. In this study, we proposed an online optimized amplitude adaptive strategy based on the particle swarm
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Optimizing Visual Stimulation Paradigms for User-Friendly SSVEP-Based BCIs IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-05 Meng Gu, Weihua Pei, Xiaorong Gao, Yijun Wang
In steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems, traditional flickering stimulation patterns face challenges in achieving a trade-off in both BCI performance and visual comfort across various frequency bands. To investigate the optimal stimulation paradigms with high performance and high comfort for each frequency band, this study systematically compared
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An OpenSim-Based Closed-Loop Biomechanical Wrist Model for Subject-Specific Pathological Tremor Simulation IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-05 Wellington C. Pinheiro, Henrique B. Ferraz, Maria Claudia F. Castro, Luciano L. Menegaldo
Objective: A pathological tremor (PT) is an involuntary rhythmic movement of varying frequency and amplitude that affects voluntary motion, thus compromising individuals’ independence. A comprehensive model incorporating PT’s physiological and biomechanical aspects can enhance our understanding of the disorder and provide valuable insights for therapeutic approaches. This study aims to build a biomechanical
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EMG-based Multi-User Hand Gesture Classification via Unsupervised Transfer Learning Using Unknown Calibration Gestures IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-04 Haojie Shi, Xinyu Jiang, Chenyun Dai, Wei Chen
The poor generalization performance and heavy training burden of the gesture classification model contribute as two main barriers that hinder the commercialization of sEMG-based human-machine interaction (HMI) systems. To overcome these challenges, eight unsupervised transfer learning (TL) algorithms developed on the basis of convolutional neural networks (CNNs) were explored and compared on a dataset
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User Training With Error Augmentation for sEMG-Based Gesture Classification IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-01 Yunus Bicer, Niklas Smedemark-Margulies, Basak Celik, Elifnur Sunger, Ryan Orendorff, Stephanie Naufel, Tales Imbiriba, Deniz Erdoğmuş, Eugene Tunik, Mathew Yarossi
We designed and tested a system for real-time control of a user interface by extracting surface electromyographic (sEMG) activity from eight electrodes in a wristband configuration. sEMG data were streamed into a machine-learning algorithm that classified hand gestures in real-time. After an initial model calibration, participants were presented with one of three types of feedback during a human-learning
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An Adaptive Hammerstein Model for FES-Induced Torque Prediction Based on Variable Forgetting Factor Recursive Least Squares Algorithm IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-29 Qinlian Yang, Yingqi Li, You Li, Manxu Zheng, Rong Song
Modeling the muscle response to functional electrical stimulation (FES) is an important step during model-based FES control system design. The Hammerstein structure is widely used in simulating this nonlinear biomechanical response. However, a fixed relationship cannot cope well with the time-varying property of muscles and muscle fatigue. In this paper, we proposed an adaptive Hammerstein model to
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A Combination Model of Shifting Joint Angle Changes With 3D-Deep Convolutional Neural Network to Recognize Human Activity IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-29 Endang Sri Rahayu, Eko Mulyanto Yuniarno, I. Ketut Eddy Purnama, Mauridhi Hery Purnomo
Research in the field of human activity recognition is very interesting due to its potential for various applications such as in the field of medical rehabilitation. The need to advance its development has become increasingly necessary to enable efficient detection and response to a wide range of movements. Current recognition methods rely on calculating changes in joint distance to classify activity
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Deep Learning for Enhanced Prosthetic Control: Real-Time Motor Intent Decoding for Simultaneous Control of Artificial Limbs IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-29 Jan Zbinden, Julia Molin, Max Ortiz-Catalan
The development of advanced prosthetic devices that can be seamlessly used during an individual’s daily life remains a significant challenge in the field of rehabilitation engineering. This study compares the performance of deep learning architectures to shallow networks in decoding motor intent for prosthetic control using electromyography (EMG) signals. Four neural network architectures, including
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3D Visual Discomfort Assessment With a Weakly Supervised Graph Convolution Neural Network Based on Inaccurately Labeled EEG IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-29 Na Lu, Xiaojie Zhao, Li Yao
Visual discomfort significantly limits the broader application of stereoscopic display technology. Hence, the accurate assessment of stereoscopic visual discomfort is a crucial topic in this field. Electroencephalography (EEG) data, which can reflect changes in brain activity, have received increasing attention in objective assessment research. However, inaccurately labeled data, resulting from the
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UI-MoCap: An Integrated UWB-IMU Circuit Enables 3D Positioning and Enhances IMU Data Transmission IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-26 Wenjuan Zhong, Lei Zhang, Zhongbo Sun, Mingjie Dong, Mingming Zhang
While inertial measurement unit (IMU)-based motion capture (MoCap) systems have been gaining popularity for human movement analysis, they still suffer from long-term positioning errors due to accumulated drift and inefficient data transmission via Wi-Fi or Bluetooth. To address this problem, this study introduces an integrated ultrawideband (UWB)-IMU system, named UI-MoCap, designed for simultaneous
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Synthetic IMU Datasets and Protocols Can Simplify Fall Detection Experiments and Optimize Sensor Configuration IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-26 Jie Tang, Bin He, Junkai Xu, Tian Tan, Zhipeng Wang, Yanmin Zhou, Shuo Jiang
Falls represent a significant cause of injury among the elderly population. Extensive research has been devoted to the utilization of wearable IMU sensors in conjunction with machine learning techniques for fall detection. To address the challenge of acquiring costly training data, this paper presents a novel method that generates a substantial volume of synthetic IMU data with minimal actual fall
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Brain Temporal-Spectral Functional Variability Reveals Neural Improvements of DBS Treatment for Disorders of Consciousness IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-22 Jiewei Lu, Jingchao Wu, Zhilin Shu, Xinyuan Zhang, Haitao Li, Siquan Liang, Jianda Han, Ningbo Yu
Deep brain stimulation (DBS) is establishing itself as a promising treatment for disorders of consciousness (DOC). Measuring consciousness changes is crucial in the optimization of DBS therapy for DOC patients. However, conventional measures use subjective metrics that limit the investigations of treatment-induced neural improvements. The focus of this study is to analyze the regulatory effects of
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Abnormal Static and Dynamic Local Functional Connectivity in First-Episode Schizophrenia: A Resting-State fMRI Study IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-22 Jie Zhou, Xiong Jiao, Qiang Hu, Lizhao Du, Jijun Wang, Junfeng Sun
Dynamic functional connectivity (FC) analyses have provided ample information on the disturbances of global functional brain organization in patients with schizophrenia. However, our understanding about the dynamics of local FC in never-treated first episode schizophrenia (FES) patients is still rudimentary. Dynamic Regional Phase Synchrony (DRePS), a newly developed dynamic local FC analysis method
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A Lightweight Dynamic Hand Orthosis With Sequential Joint Flexion Movement for Postoperative Rehabilitation of Flexor Tendon Repair Surgery IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-20 Chan Beom Park, Ji Sup Hwang, Hyun Sik Gong, Hyung-Soon Park
During the postoperative hand rehabilitation period, it is recommended that the repaired flexor tendons be continuously glided with sufficient tendon excursion and carefully managed protection to prevent adhesion with adjacent tissues. Thus, finger joints should be passively mobilized through a wide range of motion (ROM) with physiotherapy. During passive mobilization, sequential flexion of the metacarpophalangeal
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Decoding Multi-DoF Movements Using a CST-Based Force Generation Model With Single-DoF Training IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-20 Yang Xu, Yang Yu, Zeming Zhao, Xinjun Sheng
Recent developments in dexterous myoelectric prosthetics have established a hardware base for human-machine interfaces. Although pattern recognition techniques have seen successful deployment in gesture classification, their applications remain largely confined to certain specific discrete gestures. Addressing complex daily tasks demands an immediate need for precise simultaneous and proportional control
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Objective Neurophysiological Indices for the Assessment of Chronic Tinnitus Based on EEG Microstate Parameters IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-20 Yingying Wang, Peiying Zeng, Zhixiang Gu, Hongyu Liu, Shuqing Han, Xinran Liu, Xin Huang, Liyang Shao, Yuan Tao
Chronic tinnitus is highly prevalent but lacks precise diagnostic or effective therapeutic standards. Its onset and treatment mechanisms remain unclear, and there is a shortage of objective assessment methods. We aim to identify abnormal neural activity and reorganization in tinnitus patients and reveal potential neurophysiological markers for objectively evaluating tinnitus. By way of analyzing EEG
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Assessing Free-Living Postural Sway in Persons With Multiple Sclerosis IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-19 Brett M. Meyer, Jenna G. Cohen, Paolo DePetrillo, Melissa Ceruolo, David Jangraw, Nick Cheney, Andrew J. Solomon, Ryan S. McGinnis
Postural instability is associated with disease status and fall risk in Persons with Multiple Sclerosis (PwMS). However, assessments of postural instability, known as postural sway, leverage force platforms or wearable accelerometers, and are most often conducted in laboratory environments and are thus not broadly accessible. Remote measures of postural sway captured during daily life may provide a
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A Novel CNN-BiLSTM Ensemble Model With Attention Mechanism for Sit-to-Stand Phase Identification Using Wearable Inertial Sensors IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-19 Xin Chen, Shibo Cai, Longjie Yu, Xiaoling Li, Bingfei Fan, Mingyu Du, Tao Liu, Guanjun Bao
Sit-to-stand transition phase identification is vital in the control of a wearable exoskeleton robot for assisting patients to stand stably. In this study, we aim to propose a method for segmenting and identifying the sit-to-stand phase using two inertial sensors. First, we defined the sit-to-stand transition into five phases, namely, the initial sitting phase, the flexion momentum phase, the momentum
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A Novel Data Augmentation Approach Using Mask Encoding for Deep Learning-Based Asynchronous SSVEP-BCI IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-19 Wenlong Ding, Aiping Liu, Ling Guan, Xun Chen
Deep learning (DL)-based methods have been successfully employed as asynchronous classification algorithms in the steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) system. However, these methods often suffer from the limited amount of electroencephalography (EEG) data, leading to overfitting. This study proposes an effective data augmentation approach called EEG mask
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Cutting Edge Bionics in Highly Impaired Individuals: A Case of Challenges and Opportunities IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-16 Eric J. Earley, Jan Zbinden, Maria Munoz-Novoa, Fabian Just, Christiana Vasan, Axel Sjögren Holtz, Mona Emadeldin, Justyna Kolankowska, Björn Davidsson, Alexander Thesleff, Jason Millenaar, Stewe Jönsson, Christian Cipriani, Hannes Granberg, Paolo Sassu, Rickard Brånemark, Max Ortiz-Catalan
Highly impaired individuals stand to benefit greatly from cutting-edge bionic technology, however concurrent functional deficits may complicate the adaptation of such technology. Here, we present a case in which a visually impaired individual with bilateral burn injury amputation was provided with a novel transradial neuromusculoskeletal prosthesis comprising skeletal attachment via osseointegration
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Event-Related EEG Desynchronization Reveals Enhanced Motor Imagery From the Third Person Perspective by Manipulating Sense of Body Ownership With Virtual Reality for Stroke Patients IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-13 Xiaotian Xu, Xiaoya Fan, Jiaoyang Dong, Xiting Zhang, Zhe Song, Wei Li, Fang Pu
Virtual reality (VR)-based rehabilitation training holds great potential for post-stroke motor recovery. Existing VR-based motor imagery (MI) paradigms mostly focus on the first-person perspective, and the benefit of the third-person perspective (3PP) remains to be further exploited. The 3PP is advantageous for movements involving the back or those with a large range because of its field coverage.
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Channel Selection for Stereo- Electroencephalography (SEEG)-Based Invasive Brain-Computer Interfaces Using Deep Learning Methods IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-13 Xiaolong Wu, Guangye Li, Xin Gao, Benjamin Metcalfe, Dingguo Zhang
Brain-computer interfaces (BCIs) can enable direct communication with assistive devices by recording and decoding signals from the brain. To achieve high performance, many electrodes will be used, such as the recently developed invasive BCIs with channel numbers up to hundreds or even thousands. For those high-throughput BCIs, channel selection is important to reduce signal redundancy and invasiveness
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Multi-Branch Mutual-Distillation Transformer for EEG-Based Seizure Subtype Classification IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-13 Ruimin Peng, Zhenbang Du, Changming Zhao, Jingwei Luo, Wenzhong Liu, Xinxing Chen, Dongrui Wu
Cross-subject electroencephalogram (EEG) based seizure subtype classification is very important in precise epilepsy diagnostics. Deep learning is a promising solution, due to its ability to automatically extract latent patterns. However, it usually requires a large amount of training data, which may not always be available in clinical practice. This paper proposes Multi-Branch Mutual-Distillation (MBMD)
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Rapid-IAF: Rapid Identification of Individual Alpha Frequency in EEG Data Using Sequential Bayesian Estimation IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-12 Seitaro Iwama, Junichi Ushiba
Rapid and robust identification of the individual alpha frequency (IAF) in electroencephalogram (EEG) is an essential factor for successful brain-computer interface (BCI) use. Here we demonstrate an algorithm to determine the IAF from short-term resting-state scalp EEG data. First, we outlined the algorithm to determine IAF from short-term resting scalp EEG data and evaluated its reliability using
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IMU-Based Kinematics Estimation Accuracy Affects Gait Retraining Using Vibrotactile Cues IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-12 Nataliya Rokhmanova, Owen Pearl, Katherine J. Kuchenbecker, Eni Halilaj
Wearable sensing using inertial measurement units (IMUs) is enabling portable and customized gait retraining for knee osteoarthritis. However, the vibrotactile feedback that users receive directly depends on the accuracy of IMU-based kinematics. This study investigated how kinematic errors impact an individual’s ability to learn a therapeutic gait using vibrotactile cues. Sensor accuracy was computed
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Gait Intention Prediction Using a Lower-Limb Musculoskeletal Model and Long Short-Term Memory Neural Networks IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-12 Qingyao Bian, Marco Castellani, Duncan Shepherd, Jinming Duan, Ziyun Ding
The prediction of gait motion intention is essential for achieving intuitive control of assistive devices and diagnosing gait disorders. To reduce the cost associated with using multimodal signals and signal processing, we proposed a novel method that integrates machine learning with musculoskeletal modelling techniques for the prediction of time-series joint angles, using only kinematic signals. Additionally
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Neural Network Dynamics and Brain Oscillations Underlying Aberrant Inhibitory Control in Internet Addiction IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-09 Yi-Li Tseng, Yu-Kai Su, Wen-Jiun Chou, Makoto Miyakoshi, Ching-Shu Tsai, Chia-Jung Li, Sheng-Yu Lee, Liang-Jen Wang
Previous studies have reported a role of alterations in the brain’s inhibitory control mechanism in addiction. Mounting evidence from neuroimaging studies indicates that its key components can be evaluated with brain oscillations and connectivity during inhibitory control. In this study, we developed an internet-related stop-signal task with electroencephalography (EEG) signal recorded to investigate
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Classification of Action Potentials With High Variability Using Convolutional Neural Network for Motor Unit Tracking IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-09 Yixin Li, Yang Zheng, Guanghua Xu, Sicong Zhang, Renghao Liang, Run Ji
The reliable classification of motor unit action potentials (MUAPs) provides the possibility of tracking motor unit (MU) activities. However, the variation of MUAP profiles caused by multiple factors in realistic conditions challenges the accurate classification of MUAPs. The goal of this study was to propose an effective method based on the convolutional neural network (CNN) to classify MUAPs with
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The Effects of Immersion and Visuo-Tactile Stimulation on Motor Imagery in Stroke Patients are Related to the Sense of Ownership IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-09 Zhe Song, Xiting Zhang, Xiaotian Xu, Jiaoyang Dong, Wei Li, Yih-Kuen Jan, Fang Pu
Visual guided motor imagery (MI) is commonly used in stroke rehabilitation, eliciting event-related desynchronization (ERD) in EEG. Previous studies found that immersion level and visuo-tactile stimulation could modulate ERD during visual guided MI, and both of two factors could also improve sense of ownership (SOO) over target limb (or body). Additionally, the relationship was also reported between
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A Minimal and Multi-Source Recording Setup for Ankle Joint Kinematics Estimation During Walking Using Only Proximal Information From Lower Limb IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-09 Rami Mobarak, Andrea Tigrini, Federica Verdini, Ali H. Al-Timemy, Sandro Fioretti, Laura Burattini, Alessandro Mengarelli
In this study, a minimal setup for the ankle joint kinematics estimation is proposed relying only on proximal information of the lower-limb, i.e. thigh muscles activity and joint kinematics. To this purpose, myoelectric activity of Rectus Femoris (RF), Biceps Femoris (BF), and Vastus Medialis (VM) were recorded by surface electromyography (sEMG) from six healthy subjects during unconstrained walking
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Multi Degree of Freedom Hybrid FES and Robotic Control of the Upper Limb IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-02-08 Nathan Dunkelberger, Skye A. Carlson, Jeffrey Berning, Eric M. Schearer, Marcia K. O’Malley
Individuals who have suffered a spinal cord injury often require assistance to complete daily activities, and for individuals with tetraplegia, recovery of upper-limb function is among their top priorities. Hybrid functional electrical stimulation (FES) and exoskeleton systems have emerged as a potential solution to provide upper limb movement assistance. These systems leverage the user’s own muscles