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.
A comprehensive review of eeg-based brain–computer interface paradigms.Journal of Neural Engineering, 16(1):011001, jan 2019
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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.