Combining direct STFT input, increased STFT overlap, and balanced batching, TFOC-Net reports cross-subject motor imagery accuracies of 67.60%, 65.96%, and 80.22% on BCI Competition IV datasets IV-1, IV-2A, and IV-2B.
Cross-Subject EEG Signal Classification with Deep Neural Networks Applied to Motor Imagery,
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TFOC-Net: A Short-time Fourier Transform-based Deep Learning Approach for Enhancing Cross-Subject Motor Imagery Classification
Combining direct STFT input, increased STFT overlap, and balanced batching, TFOC-Net reports cross-subject motor imagery accuracies of 67.60%, 65.96%, and 80.22% on BCI Competition IV datasets IV-1, IV-2A, and IV-2B.