Learning to decode human emotions from event-related potentials

Abstract Reported works on electroencephalogram (EEG)-based emotion recognition systems generally employ the principles of supervised learning to build subject-dependent (single/intra-subject) models. Building subject-independent (multiple/inter-subject) models is a harder problem due to the EEG data variability between subjects. The contribution of this paper is twofold. First, we provide a framework for selection of a small number of basic temporal features, event-related potential (ERP) amplitudes, and latencies that are sufficiently robust to discriminate emotion states across multiple subjects. Second, we test comparatively the feasibility of six standard unsupervised (clustering) techniques to build intra-subject and inter-subject models to discriminate emotion valence in the ERPs collected while subjects were viewing high arousal images with positive or negative emotional content.

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