CFSPMNet improves cross-subject MI-EEG decoding accuracy for stroke patients to 68-73% by combining Fourier-guided Mamba networks with calibrated prototype matching, outperforming baselines by 5-8 points.
Eegnet: a compact convolutional neural network for eeg-based brain–computer interfaces.Journal of Neural Engineering, 15(5):056013, jul 2018
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PA-TCNet improves cross-subject motor imagery EEG decoding accuracy in stroke patients to 66.56% and 72.75% on two datasets by pathology-aware rhythmic state modeling and physiology-constrained pseudo-label refinement.
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CFSPMNet: Cross-subject Fourier-guided Spatial-Patch Mamba Network for EEG Motor Imagery Decoding in Stroke Patients
CFSPMNet improves cross-subject MI-EEG decoding accuracy for stroke patients to 68-73% by combining Fourier-guided Mamba networks with calibrated prototype matching, outperforming baselines by 5-8 points.
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PA-TCNet: Pathology-Aware Temporal Calibration with Physiology-Guided Target Refinement for Cross-Subject Motor Imagery EEG Decoding in Stroke Patients
PA-TCNet improves cross-subject motor imagery EEG decoding accuracy in stroke patients to 66.56% and 72.75% on two datasets by pathology-aware rhythmic state modeling and physiology-constrained pseudo-label refinement.