Attempted Prediction of Emotional Valence from EEG Using Multidimensional Directed Information

Abstract

Quantitative measurement of a person’s emotional state can aid performance in a number of areas, such as human-machine interactions, and psychological research. Electroencephalogram (EEG) data has shown potential as a predictor of emotional valence based on asymmetric activation patterns between the left and right hemispheres of the prefrontal cortex. Multidimensional directed information (MDI) is a computational tool that allows the measurement of information content transferred between different signals in a connected system, and has previously seen applications in EEG-based affective measurement in order to detect the presence of an emotional response. This study aimed to use MDI with EEG data from published datasets in order to derive a directional bias metric as a predictor for emotional valence based on frontal hemisphere asymmetry. Two methods of MDI computation were attempted; significant differences were observed in results between the two, suggesting possible errors in implementation. Neither method yielded output correlating with valence.