Low-rank tensor decomposition for cross-bispectral analysis of EEG data

The proposed low-rank decomposition offers a principled and computationally efficient approach for reducing and interpreting cross-bispectral EEG data. By bridging tensor decomposition with biophysical source models, it enables insights into complex neural coupling that are inaccessible with standard matrix or tensor techniques.