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.
Eeg conformer: Convolutional transformer for eeg decoding and visualization.IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31:710–719
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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.
CAIA framework improves zero-shot brain-to-image retrieval accuracy by adaptively blurring visuals to match neural granularity and screening task-relevant EEG oscillations.
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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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.
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Neural Visual Decoding via Cognitive guided Adaptive Blurring and Information Constrained Alignment
CAIA framework improves zero-shot brain-to-image retrieval accuracy by adaptively blurring visuals to match neural granularity and screening task-relevant EEG oscillations.