A data-driven validation of frontal EEG asymmetry using a consumer device

Abstract

Affective computing requires a reliable method to obtain real time information regarding affective state, and one of the promising avenues is via electroencephalography (EEG). We have performed a study intended to test whether a low cost EEG device targeted at consumers can be used to measure extreme emotional valence. One of the most studied frameworks related to the way affect is reflected in EEG is based on frontal hemispheric asymmetry. Our results indicate that a simple replication of the methods derived from this hypothesis might not be sufficient. However, using a data-driven approach based on feature engineering and machine learning, we describe a method that can reliably measure valence with the EPOC device. We discuss our study in the context of the theoretical and empirical background for frontal asymmetry.