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 neuroscientific knowledge. We propose a statistically-motivated electrode/feature selection procedure, based on Cohen's effect size f~2. 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 f~2 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.
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