Emotion Recognition Using Ensemble Bagged Tree Classifier and Electroencephalogram Signals

Abstract

Introduction: Emotion plays a key role in our daily life and work, especially in decision making, as people's moods can influence their mode of communication, behaviour or productivity. Emotion recognition has attracted some research works and medical imaging technology offers tools for emotion classification. Aims: The aim of this work is to develop a machine learning technique for recognizing emotion based on Electroencephalogram (EEG) data Materials and Methods: Experimentation was based on a publicly available EEG Dataset for Emotion Analysis using Physiological (DEAP). The data comprises of EEG signals acquired from thirty two adults while watching forty 40 different musical video clips of one minute each. Participants rated each video in terms of four emotional states, namely, arousal, valence, like/dislike and dominance. We extracted some features from the dataset using Discrete Wavelet Transforms to extract wavelet energy, wavelet entropy, and standard deviation. We then classified the extracted features into four emotional states, namely, High Valence/High Arousal, High Valance/Low Arousal, Low Valence/High Arousal, and Low Valence/Low Arousal using Ensemble Bagged Trees. Results: Ensemble Bagged Trees gave sensitivity, specificity, and accuracy of 97.54%, 99.21%, and 97.80% respectively. Support Vector Machine and Ensemble Boosted Tree gave similar results. Conclusion: Our results showed that machine learning classification of emotion using EEG data is very promising. This can help in the treatment of patients, especially those with expression problems like Amyotrophic Lateral Sclerosis which is a muscle disease, the real emotional state of patients will help doctors to provide appropriate medical care. Keywords: Electroencephalogram, Emotions Recognition, Ensemble Classification, Ensemble Bagged Trees, Machine Learning