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.
Deep learning with convolutional neural networks for eeg decoding and visualization.Human Brain Mapping, 38(11):5391–5420
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NAKUL achieves 91.7% accuracy on motor imagery EEG with 28% fewer parameters than EEG-Conformer by using dynamic kernel generation, spectral context modeling, and graph-guided spatial attention.
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.
citing papers explorer
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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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NAKUL-Med: Spectral-Graph State Space Models with Dynamics Kernels for Medical Signals
NAKUL achieves 91.7% accuracy on motor imagery EEG with 28% fewer parameters than EEG-Conformer by using dynamic kernel generation, spectral context modeling, and graph-guided spatial attention.
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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.