Classification of basic human emotions from electroencephalography data

Abstract

This paper explores the combination of known signal processing techniques to analyze electroencephalography (EEG) data for the classification of a set of basic human emotions. An Emotiv EPOC headset with 16 electrodes was used to measure EEG data from a population of 24 subjects who were presented an audiovisual stimuli designed to evoke 4 emotions (rage, fear, fun and neutral). Raw data was preprocessed to eliminate noise, interference and physiologic artifacts. Discrete Wavelet Transform (DWT) was used to extract its main characteristics and define relevant features. Classification was performed using different algorithms and results compared. The best results were obtained when using meta-learning techniques with classification errors at 5 %. Final conclusions and future work are discussed.