Emotion Recognition from Brain Signals While Subjected to Music Videos

Abstract

Emotions are simple, yet complex windows to the brain. Music and emotions are associated closely together. There are few things that stimulate the brain the way music does. It can be used as a powerful tool to regulate one’s emotions. In recent years, emotion detection using brain waves has become an active topic of research. Various researchers are implementing different feature extraction techniques and machine learning models to classify the emotion by predicting the measurement of the electroencephalography signals. Many researchers are working on improving the accuracy of this problem and employing different techniques. In our study, we looked into achieving good scores by trying to predict the actual 4 emotional quadrants of the 2 dimensional Valence-Arousal plane. We evaluated and looked into various feature extraction approaches, modeling approaches and tried to combine best practices in our approach. We used the publicly available DEAP dataset for this study. Features from multiple domains were extracted from the EEG data and various statistical metrics and measures were extracted per channel. In our proposed approach, a one-dimensional convolutional neural network and a two-dimensional convolution neural network model were combined and fed through a neural network to classify the four quadrants of emotions. We did extensive and systematic experiments on the proposed approach over the benchmark dataset. The research findings that may be of significant interest to the user adaptation and personalization are presented in this study.