Effect-size-based electrode and feature selection for emotion recognition from EEG

Emotion recognition from EEG signals allows the direct assessment of the 'inner' state of the user which is considered an important factor in Human-Machine-Interaction. Given the vast amount of possible features from scalp recordings and the high variance between subjects, a major challenge is to select electrodes and features that separate classes well. In most cases, this decision is made based on neuro-scientific knowledge. We propose a statistically-motivated electrode/feature selection procedure, based on Cohen's effect size f². We compare inter- and intra-individual selection on a self-recorded database. Classification is evaluated using quadratic discriminant analysis (QDA). We found both feature selection versions based on f² yield comparable results. While highest accuracies up to 57,5% (5 classes) are reached by applying intra-individual selection, inter-individual analysis successfully finds features that perform with lower variance in recognition rates across subjects than combinations of electrodes/features suggested in literature.