GACL-Net:Hybrid Deep Learning Framework for Accurate Motor Imagery Classification in Stroke Rehabilitation  

作　　者：Chayut Bunterngchit Laith H.Baniata Mohammad H.Baniata Ashraf ALDabbas Mohannad A.Khair Thanaphon Chearanai Sangwoo Kang 

机构地区：[1]State Key Laboratory of Multimodal Artificial Intelligence Systems,Institute of Automation,Chinese Academy of Sciences,Beijing,100190,China [2]School of Computing,Gachon University,Seongnam,13120,Republic of Korea [3]Computer Science Department,Faculty of Information Technology,The World Islamic Sciences and Education University,Amman,11947,Jordan [4]Intelligent Systems Department,Faculty of Artificial Intelligence,Al-Balqa Applied University,Al-Salt,19117,Jordan [5]IT Infrastructure Department,Qatrana Cement Company,Amman,11821,Jordan [6]Division of Industrial and Logistics Engineering Technology,Faculty of Engineering and Technology,King Mongkut’s University of Technology North Bangkok,Rayong Campus,Rayong,21120,Thailand

出　　处：《Computers, Materials & Continua》2025年第4期517-536,共20页计算机、材料和连续体(英文)

基　　金：supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF);funded by the Ministry of Science and ICT under Grant NRF-2022R1A2C1005316.

摘　　要：Stroke is a leading cause of death and disability worldwide,significantly impairing motor and cognitive functions.Effective rehabilitation is often hindered by the heterogeneity of stroke lesions,variability in recovery patterns,and the complexity of electroencephalography(EEG)signals,which are often contaminated by artifacts.Accurate classification of motor imagery(MI)tasks,involving the mental simulation of movements,is crucial for assessing rehabilitation strategies but is challenged by overlapping neural signatures and patient-specific variability.To address these challenges,this study introduces a graph-attentive convolutional long short-term memory(LSTM)network(GACL-Net),a novel hybrid deep learning model designed to improve MI classification accuracy and robustness.GACL-Net incorporates multi-scale convolutional blocks for spatial feature extraction,attention fusion layers for adaptive feature prioritization,graph convolutional layers to model inter-channel dependencies,and bidi-rectional LSTM layers with attention to capture temporal dynamics.Evaluated on an open-source EEG dataset of 50 acute stroke patients performing left and right MI tasks,GACL-Net achieved 99.52%classification accuracy and 97.43%generalization accuracy under leave-one-subject-out cross-validation,outperforming existing state-of-the-art methods.Additionally,its real-time processing capability,with prediction times of 33–56 ms on a T4 GPU,underscores its clinical potential for real-time neurofeedback and adaptive rehabilitation.These findings highlight the model’s potential for clinical applications in assessing rehabilitation effectiveness and optimizing therapy plans through precise MI classification.

关 键 词：Motor imagery EEG stroke rehabilitation deep learning brain-computer interface 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]