I don't see how anything here will get survivors recovered! Great word salad, though!
GACL-Net: Hybrid Deep Learning Framework for Accurate Motor Imagery Classification in Stroke Rehabilitation
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Under a Creative Commons license
Open access
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
bidirectional 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.
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