Abstract:

Throughout various disciplines, emotion recognition continues to be an essential subject of study. With the advancement of machine learning methods, accurate emotion recognition from different data modalities (facial images, brain EEG signals) has become possible. Success of EEG-based emotion recognition systems depends on efficient feature extraction and pre/postprocessing of signals. Main objective of this study is to analyze the efficacy of multiple-instance learning (MIL) on postprocessing features of EEG signals using three different domains (time, frequency, time-frequency) for human emotion classification. Methods and results are presented for single-trial classification of valence (V), arousal (A), and dominance (D) ratings from EEG signals obtained with audio (A), video (V), and audio-video (AV) stimulus using alpha, beta and gamma bands. High accuracy was observed with both binary and multiclass classification of the AV stimulus. Findings in this study suggest that MIL applied on frequency features yields efficient results on EEG emotion recognition.

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