EEG-based Positive-Negative Emotion Classification Using Machine Learning Techniques

Abstract

The aim of this study is to find useful electrodes for positive-negative emotion classification based on EEG. We collected EEG signals from 30 people aged 19-38 using 14 electrodes. We used two movies for positive and negative emotions. First, we extracted the power spectrum from the EEG data, normalized the data, and extracted frequency-domain statistical parameters therefrom. When the features were applied to Random Forests (RF), 85.4%, 83.8%, and 83.4% accuracy was obtained for P8, P7, and FC6 electrodes, respectively. This indicates that the P8, P7 and FC6 electrodes are the useful electrode in positive-negative emotion classification.