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Real-time precise targeting of the subthalamic nucleus via transfer learning in a rat model of Parkinson’s disease based on microelectrode arrays IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-04-24 Qianli Jia, Luyi Jing, Yuxin Zhu, Meiqi Han, Peiyao Jiao, Yu Wang, Zhaojie Xu, Yiming Duan, Mixia Wang, Xinxia Cai
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Exploration and Application of a Muscle Fatigue Assessment Model Based on NMF for Multi-Muscle Synergistic Movements IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-04-24 Jinxu Yu, Lijie Zhang, Yihao Du, Xiaoran Wang, Jianhua Yan, Jie Chen, Ping Xie
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Illusory Directional Sensation Induced by Asymmetric Vibrations Influences Sense of Agency and Velocity in Wrist Motions IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-04-24 Takeshi Tanabe, Hidekazu Kaneko
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Resist-as-needed ADL Training with SPINLDE for Patients with Tremor IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-04-23 Nikhil Tej Kantu, Ryan Osswald, Amit Kandel, Jiyeon Kang
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BlazePose-Seq2Seq: Leveraging Regular RGB Cameras for Robust Gait Assessment IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-04-22 Abdul Aziz Hulleck, Aamna Alshehhi, Marwan El Rich, Raviha Khan, Rateb Katmah, Mahdi Mohseni, Navid Arjmand, Kinda Khalaf
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E-BabyNet: Enhanced Action Recognition of Infant Reaching in Unconstrained Environments IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-04-22 Amel Dechemi, Konstantinos Karydis
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Alignment-Based Adversarial Training (ABAT) for Improving the Robustness and Accuracy of EEG-Based BCIs IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-04-22 Xiaoqing Chen, Ziwei Wang, Dongrui Wu
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A Subject-Specific Attention Index Based on the Weighted Spectral Power IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-04-22 Guiying Xu, Zhenyu Wang, Xi Zhao, Ruxue Li, Ting Zhou, Tianheng Xu, Honglin Hu
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Simplifying Multimodal with Single EOG Modality for Automatic Sleep Staging IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-04-18 Yangxuan Zhou, Sha Zhao, Jiquan Wang, Haiteng Jiang, Zhenghe Yu, Shijian Li, Tao Li, Gang Pan
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A Siamese Convolutional Neural Network for Identifying mild Traumatic Brain Injury and Predicting Recovery IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-04-18 Fatemeh Koochaki, Laleh Najafizadeh
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Automatic Detection of Scalp High-Frequency Oscillations Based on Deep Learning IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-04-16 Yutang Li, Dezhi Cao, Junda Qu, Wei Wang, Xinhui Xu, Lingyu Kong, Jianxiang Liao, Wenhan Hu, Kai Zhang, Jihan Wang, Chunlin Li, Xiaofeng Yang, Xu Zhang
Scalp high-frequency oscillations (sHFOs) are a promising non-invasive biomarker of epilepsy. However, the visual marking of sHFOs is a time-consuming and subjective process, existing automatic detectors based on single-dimensional analysis have difficulty with accurately eliminating artifacts and thus do not provide sufficient reliability to meet clinical needs. Therefore, we propose a high-performance
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Improving SSVEP-BCI Performance Through Repetitive Anodal tDCS-Based Neuromodulation: Insights From Fractal EEG and Brain Functional Connectivity IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-04-16 Shangen Zhang, Hongyan Cui, Yong Li, Xiaogang Chen, Xiaorong Gao, Cuntai Guan
This study embarks on a comprehensive investigation of the effectiveness of repetitive transcranial direct current stimulation (tDCS)-based neuromodulation in augmenting steady-state visual evoked potential (SSVEP) brain-computer interfaces (BCIs), alongside exploring pertinent electroencephalography (EEG) biomarkers for assessing brain states and evaluating tDCS efficacy. EEG data were garnered across
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Muscle Synergy Plasticity in Motor Function Recovery after Stroke IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-04-15 Yixuan Sheng, Jixian Wang, Gansheng Tan, Hui Chang, Qing Xie, Honghai Liu
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Multi-Scale Masked Autoencoders for Cross-Session Emotion Recognition IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-04-15 Miaoqi Pang, Hongtao Wang, Jiayang Huang, Chi-Man Vong, Zhiqiang Zeng, Chuangquan Chen
Affective brain-computer interfaces (aBCIs) have garnered widespread applications, with remarkable advancements in utilizing electroencephalogram (EEG) technology for emotion recognition. However, the time-consuming process of annotating EEG data, inherent individual differences, non-stationary characteristics of EEG data, and noise artifacts in EEG data collection pose formidable challenges in developing
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Socially Assistive Robot for Stroke Rehabilitation: A Long-Term in-the-Wild Pilot Randomized Controlled Trial IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-04-10 Ronit Feingold-Polak, Oren Barzel, Shelly Levy-Tzedek
Socially assistive robots (SARs) have been suggested as a platform for post-stroke training. It is not yet known whether long-term interaction with a SAR can lead to an improvement in the functional ability of individuals post-stroke. The aim of this pilot study was to compare the changes in motor ability and quality of life following a long-term intervention for upper-limb rehabilitation of post-stroke
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A Time-Local Weighted Transformation Recognition Framework for Steady State Visual Evoked Potentials Based Brain–Computer Interfaces IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-04-10 Ke Qin, Ren Xu, Shurui Li, Xingyu Wang, Andrzej Cichocki, Jing Jin
Canonical correlation analysis (CCA), Multivariate synchronization index (MSI), and their extended methods have been widely used for target recognition in Brain-computer interfaces (BCIs) based on Steady State Visual Evoked Potentials (SSVEP), and covariance calculation is an important process for these algorithms. Some studies have proved that embedding time-local information into the covariance can
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Multi-Stimulus Least-Squares Transformation With Online Adaptation Scheme to Reduce Calibration Effort for SSVEP-Based BCIs IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-04-10 Dandan Li, Xuedong Wang, Mingliang Dou, Yao Zhao, Xiaohong Cui, Jie Xiang, Bin Wang
Steady-state visual evoked potential (SSVEP), one of the most popular electroencephalography (EEG)-based brain-computer interface (BCI) paradigms, can achieve high performance using calibration-based recognition algorithms. As calibration-based recognition algorithms are time-consuming to collect calibration data, the least-squares transformation (LST) has been used to reduce the calibration effort
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Wearable Motion Analysis System for Thoracic Spine Mobility with Inertial Sensors IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-04-09 Chenyao Zhu, Lan Luo, Rui Li, Junhui Guo, Qining Wang
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Explainable Deep-Learning Prediction for Brain–Computer Interfaces Supported Lower Extremity Motor Gains Based on Multistate Fusion IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-04-05 Ping-Ju Lin, Wei Li, Xiaoxue Zhai, Zhibin Li, Jingyao Sun, Quan Xu, Yu Pan, Linhong Ji, Chong Li
Predicting the potential for recovery of motor function in stroke patients who undergo specific rehabilitation treatments is an important and major challenge. Recently, electroencephalography (EEG) has shown potential in helping to determine the relationship between cortical neural activity and motor recovery. EEG recorded in different states could more accurately predict motor recovery than single-state
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A Deep Quantum Convolutional Neural Network Based Facial Expression Recognition For Mental Health Analysis IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-04-04 Sanoar Hossain, Saiyed Umer, Ranjeet Kumar Rout, Hasan Al Marzouqi
The purpose of this work is to analyze how new technologies can enhance clinical practice while also examining the physical traits of emotional expressiveness of face expression in a number of psychiatric illnesses. Hence, in this work, an automatic facial expression recognition system has been proposed that analyzes static, sequential, or video facial images from medical healthcare data to detect
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A Multi-modal Classification Method for Early Diagnosis of Mild Cognitive Impairment and Alzheimer’s Disease Using Three Paradigms with Various Task Difficulties IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-04-03 Sheng Chen, Chutian Zhang, Hongjun Yang, Liang Peng, Haiqun Xie, Zeping Lv, Zeng-Guang Hou
Alzheimer’s Disease (AD) accounts for the majority of dementia, and Mild Cognitive Impairment (MCI) is the early stage of AD. Early and accurate diagnosis of dementia plays a vital role in more targeted treatments and effectively halting disease progression. However, the clinical diagnosis of dementia requires various examinations, which are expensive and require a high level of expertise from the
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VSSI-GGD: A Variation Sparse EEG Source Imaging Approach Based on Generalized Gaussian Distribution IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-04-02 Ke Liu, Shu Peng, Chengzhi Liang, Zhuliang Yu, Bin Xiao, Guoyin Wang, Wei Wu
Electroencephalographic (EEG) source imaging (ESI) is a powerful method for studying brain functions and surgical resection of epileptic foci. However, accurately estimating the location and extent of brain sources remains challenging due to noise and background interference in EEG signals. To reconstruct extended brain sources, we propose a new ESI method called Variation Sparse Source Imaging based
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Continuous Motion Intention Prediction Using sEMG for Upper-Limb Rehabilitation: A Systematic Review of Model-Based and Model-Free Approaches IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-04-01 Zijun Wei, Zhi-Qiang Zhang, Sheng Quan Xie
Upper limb functional impairments persisting after stroke significantly affect patients’ quality of life. Precise adjustment of robotic assistance levels based on patients’ motion intentions using sEMG signals is crucial for active rehabilitation. This paper systematically reviews studies on continuous prediction of upper limb single joints and multi-joint combinations motion intention using Model-Based
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Willed Attentional Selection of Visual Features: An EEG Study IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-04-01 Jingyi Wang, Jiaqi Wang, Jingyi Hu, Shanbao Tong, Xiangfei Hong, Junfeng Sun
Visual selective attention studies generally tend to apply cuing paradigms to instructively direct observers’ attention to certain locations, features or objects. However, in real situations, attention in humans often flows spontaneously without any specific instructions. Recently, a concept named “willed attention” was raised in visuospatial attention, in which participants are free to make volitional
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Does Exerting Grasps Involve a Finite Set of Muscle Patterns? A Study of Intra- and Intersubject Variability of Forearm sEMG Signals in Seven Grasp Types IEEE Trans. Netural Syst. Rehabil. Eng. (IF 4.9) Pub Date : 2024-03-29 Néstor J. Jarque-Bou, Margarita Vergara, Joaquín L. Sancho-Bru
Surface Electromyography (sEMG) signals are widely used as input to control robotic devices, prosthetic limbs, exoskeletons, among other devices, and provide information about someone’s intention to perform a particular movement. However, the redundant action of 32 muscles in the forearm and hand means that the neuromotor system can select different combinations of muscular activities to perform the
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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
The motor imagery brain-computer interface (MI-BCI) based on electroencephalography (EEG) is a widely used human-machine interface paradigm. However, due to the non-stationarity and individual differences among subjects in EEG signals, the decoding accuracy is limited, affecting the application of the MI-BCI. In this paper, we propose the EISATC-Fusion model for MI EEG decoding, consisting of inception
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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 Waugh Ruggles, Scott L. Delp, C. Karen Liu, Steven H. Collins
Exoskeletons are a burgeoning technology with many possible applications to improve human life; focusing the effort of exoskeleton research and development on the most important features is essential for facilitating adoption and maximizing positive societal impact. To identify important focus areas for exoskeleton research and development, we conducted a survey with 154 potential users (older adults)
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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
The magnetoencephalogram (MEG) based on array optically pumped magnetometers (OPMs) has the potential of replacing conventional cryogenic superconducting quantum interference device. Phase synchronization is a common method for measuring brain oscillations and functional connectivity. Verifying the feasibility and fidelity of OPM-MEG in measuring phase synchronization will help its widespread application
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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
The electroencephalogram-based (EEG) brain-computer interface (BCI) has garnered significant attention in recent research. However, the practicality of EEG remains constrained by the lack of efficient EEG decoding technology. The challenge lies in effectively translating intricate EEG into meaningful, generalizable information. EEG signal decoding primarily relies on either time domain or frequency
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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 Andrea Pergolini, Thomas Bowman, Tiziana Lencioni, Alberto Marzegan, Mario Meloni, Maria Chiara Carrozza, Emilio Trigili, Nicola Vitiello, Davide Cattaneo, Simona Crea
Individuals with Parkinson’s disease (PD) are characterized by gait and balance disorders limiting their independence and quality of life. Home-based rehabilitation programs, combined with drug therapy, demonstrated to be beneficial in the daily-life activities of PD subjects. Sensorized shoes can extract balance- and gait-related data in home-based scenarios and allow clinicians to monitor subjects’
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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
Continuous Theta Burst Stimulation (cTBS) has been shown to modulate cortical oscillations and induce cortical inhibitory effects. Electroencephalography (EEG) studies have shown some immediate effects of cTBS on brain activity. To investigate both immediate effects and short-term effects of cTBS on dynamic brain changes, cTBS was applied to 22 healthy participants over their left motor cortex. We
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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 Jente Willaert, Kaat Desloovere, Anja Van Campenhout, Lena H. Ting, Friedl De Groote
Joint hyper-resistance is a common symptom in neurological disorders. It has both neural and non-neural origins, but it has been challenging to distinguish different origins based on clinical tests alone. Combining instrumented tests with parameter identification based on a neuromechanical model may allow us to dissociate the different origins of joint hyper-resistance in individual patients. However
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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
The primary goal of rehabilitation for individuals with lower limb amputation, particularly those with unilateral transfemoral amputation (uTFA), is to restore their ability to walk independently. Effective control of the center of pressure (COP) during gait is vital for maintaining balance and stability, yet it poses a significant challenge for individuals with uTFA. This study aims to study the COP
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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
Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have emerged as a prominent technology due to their high information transfer rate, rapid calibration time, and robust signal-to-noise ratio. However, a critical challenge for practical applications is performance degradation caused by user fatigue during prolonged use. This work proposes novel methods to address this
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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
Sensorimotor impairment is a prevalent condition requiring effective rehabilitation strategies. This study introduces a novel wearable device for Mindful Sensorimotor Training (MiSMT) designed for sensory and motor rehabilitation. Our MiSMT device combines motor training using myoelectric pattern recognition along sensory training using two tactile displays. This device offers a comprehensive solution
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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
Brain-computer interfaces (BCIs) are anticipated to improve the efficacy of rehabilitation for people with motor disabilities. However, applying BCI in clinical practice is still a challenge due to the great diversity of patients. In the current study, a novel action observation (AO) based BCI was proposed and tested on stroke patients. Ten non-hemineglect patients and ten hemineglect patients were
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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
Depression ranks among the most prevalent mood-related psychiatric disorders. Existing clinical diagnostic approaches relying on scale interviews are susceptible to individual and environmental variations. In contrast, the integration of neuroimaging techniques and computer science has provided compelling evidence for the quantitative assessment of major depressive disorder (MDD). However, one of the
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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 M. C. Vendemia
Research suggests that structural and functional changes within the brain are associated with chronic low back pain, and these cortical alterations might contribute to impaired sensorimotor control of the trunk and hips in this population. However, linking sensorimotor brain changes with altered movement of the trunk and hips during task-based neuroimaging presents significant challenges. An MRI-safe
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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
Reducing energy consumption during walking is a critical goal for transtibial amputees. The study presents the evaluation of a semi-active prosthesis with five transtibial amputees. The prosthesis has a low-power actuator integrated in parallel into an energy-storing-and-releasing foot. The actuator is controlled to compress the foot during the stance phase, supplementing the natural compression due
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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
Motor brain-computer interfaces (BCIs) have gained growing research interest in motor rehabilitation, restoration, and prostheses control. Decoding upper-limb movement direction with noninvasive BCIs has been extensively investigated. However, few of them address the intervention of cognitive distraction that impairs decoding performance in practice. In this study, we propose a novel decoding model
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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
Nowadays, numerous countries are facing the challenge of aging population. Additionally, the number of people with reduced mobility due to physical illness is increasing. In response to this issue, robots used for walking assistance and sit-to-stand (STS) transition have been introduced in nursing to assist these individuals with walking. Given the shared characteristics of these robots, this paper
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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
Transcranial magnetic stimulation (TMS) is often applied to the motor cortex to stimulate a collection of motor evoked potentials (MEPs) in groups of peripheral muscles. The causal interface between TMS and MEP is the selective activation of neurons in the motor cortex; moving around the TMS ‘spot’ over the motor cortex causes different MEP responses. A question of interest is whether a collection
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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