Asymmetric Spatial Pattern for EEG-based emotion detection

Feature extraction has been a crucial and challenging task for EEG-based BCI applications mainly due to the problems of high-dimensionality and high noise level of EEG signals. In this paper we developed a novel feature extraction algorithm for EEG-based emotion detection problem. The proposed algorithm is derived from viewing EEG signals as the activation/deactivation of sources specific to the brain activities of interest. For binary classification problem, to be more specific, we consider the EEG signals for the two types of brain activities as characterized by the activation/deactivation of two discriminatory sources in the brain, with one source activated and the other one deactivated for one particular type of brain activities. The proposed algorithm, termed Asymmetric Spatial Pattern (ASP), extracts pairs of spatial filters, with each filter corresponding to only one of the two sources. The idea of ASP is neurologically plausible for certain situations. For example, according to the valence hypothesis of emotion, the left hemisphere is more activated in positive emotions and the right hemisphere is more activated in negative emotions. The effectiveness of the proposed algorithm is confirmed by application to real data for two types of EEG-based emotion detection problems: arousal detection (strong v.s. calm), and valence detection (positive v.s. negative). Experimental results on the real data also show that some of the asymmetric spatial patterns by ASP are consistent with the current neurophysiological findings on brain emotion processing.