Tucker Tensor Decomposition of Multi-session EEG Data

The Tucker model is a tensor decomposition method for multi-way data analysis. However, its application in the area of multichannel electroencephalogram (EEG) is rare and often without detailed electrophysiological interpretation of the obtained results. In this work, we apply the Tucker model to a set of multi-channel EEG data recorded over several separate sessions of motor imagery training. We consider a three-way and four-way version of the model and investigate its effect when applied to multi-session data. We discuss the advantages and disadvantages of both Tucker model approaches.