Tensor decomposition of EEG signals for transfer learning applications (2024)
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1 Abstract
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).
2 NOTES
The idea of this paper is interesting, the authors propose a tensor decomposition from the EEG signal (after a filter bank is used) into three matrices, where their values all have some relation to the session, channel and frequency. However, I can’t see any way to relate to my work, I just don’t get it.