Deans' stroke musings: GACL-Net: Hybrid Deep Learning Framework for Accurate Motor Imagery Classification in Stroke Rehabilitatio

Changing stroke rehab and research worldwide now.Time is Brain! trillions and trillions of neurons that DIE each day because there are NO effective hyperacute therapies besides tPA(only 12% effective). I have 523 posts on hyperacute therapy, enough for researchers to spend decades proving them out. These are my personal ideas and blog on stroke rehabilitation and stroke research. Do not attempt any of these without checking with your medical provider. Unless you join me in agitating, when you need these therapies they won't be there.

What this blog is for:

My blog is not to help survivors recover, it is to have the 10 million yearly stroke survivors light fires underneath their doctors, stroke hospitals and stroke researchers to get stroke solved. 100% recovery. The stroke medical world is completely failing at that goal, they don't even have it as a goal. Shortly after getting out of the hospital and getting NO information on the process or protocols of stroke rehabilitation and recovery I started searching on the internet and found that no other survivor received useful information. This is an attempt to cover all stroke rehabilitation information that should be readily available to survivors so they can talk with informed knowledge to their medical staff. It lays out what needs to be done to get stroke survivors closer to 100% recovery. It's quite disgusting that this information is not available from every stroke association and doctors group.

Sunday, April 6, 2025

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

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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https://doi.org/10.32604/cmc.2025.060368

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.