Tensor decomposition of EEG signals for transfer learning applications

We address the recognized person-to-person Brain–Computer Interface (BCI) calibration problem and tackle session-dependency through the use of unsupervised canonical polyadic (CP) tensor decomposition. For a motor imagery task, the approach reveals universal structures within EEG data, common between subjects and prominent for a certain task. Further, we develop a novel similarity measure that includes weighting of the decomposition’s factor matrices, and argue that it is more representative than what has previously been presented in literature. The proposed similarity measure shows potential in a BCI classification task, i.e. drowsiness during simulated driving (average Pearson correlation of 0.6).