Reduced complexity space-time-frequency model for multi-channel EEG and its applications

Searching for an efficient summarization of multi-channel electroencephalogram (EEG) behavior is a challenging signal analysis problem. Recently, parallel factor analysis (PARAFAC) is reported as an efficient tool for extracting features of multi-channel EEG by simultaneously employing space-time-frequency knowledge, i.e. decomposing multi-channel EEG signal into a linear combination of its space-timefrequency feature. However, this decomposition scheme suffers from expensive computational load when applied to either long term or high number of channels EEG signals. In this paper, a reduced computational complexity space-time-frequency model for multi-channel EEG signal is proposed by dividing selected content Into segments yielding additional segment signatures. By carefully selecting the number of segments, features extracted from the proposed model are comparable with those from the conventional space-time-frequency model while the time used in computation is reduced by more than 50%. Simulation results show that the proposed model can efficiently extract eye blink artifact from background EEG. Furthermore, classification accuracy when employing the proposed model to brain computer interface (BCI) application is also comparable with the conventional model.