Robust EEG emotion classification using segment level decision fusion

In this paper we address single-trial binary classification of emotion dimensions (arousal, valence, dominance and liking) using electroencephalogram (EEG) signals that represent responses to audio-visual stimuli. We propose an innovative three step solution to this problem: (1) in contrast to the typical feature extraction on the response-level, we represent the EEG signal as a sequence of overlapping segments and extract feature vectors on the segment level; (2) transform segment level features to the response level features using projections based on a novel non-parametric nearest neighbor model; and (3) perform classification on the obtained response-level features. We demonstrate the efficacy of our approach by performing binary classification of emotion dimensions on DEAP (Dataset for Emotion Analysis using electroencephalogram, Physiological and Video Signals) and report state-of-the-art classification accuracies for all emotional dimensions.