Lightweight Transformer for EEG Classification via Balanced Signed Graph Algorithm Unrolling
We build lightweight and interpretable transformer-like neural nets by unrolling a spectral denoising algorithm for signals on a balanced signed graph—graph with no cycles of odd number of negative edges. Experiments show that our method achieves classification performance comparable to representative deep learning schemes, while employing dramatically fewer parameters.