A comparative analysis of emotion recognition using EEG signals with a channel selection technique

Abstract

Emotion can be defined as the neurophysiological changes people experience due to significant internal or external occasions. This is a mental condition that can a↵ect a person’s behavior, mood, way of life, and relationship with others. As it directly a↵ects one’s life, emotion recognition is an important subject in the area of research field. In recent years, there has been a relentless e↵ort to develop several models and datasets to detect human emotions and analyze them to understand the depth of complex human feelings and reduce error in the detection of emotions. To get better results in recognizing emotion, extensive research is needed to be done on the feature extraction methods and channel selection. While measuring the performance of di↵erent classification algorithms, it is very important to compare the results as well as preprocessing techniques. In this work, we extracted DWT wavelet features of the EEG channels from the DEAP dataset and used a statistical parameter Root Sum Square (RSS) to reduce the dimension of the features. Then we applied a channel selection algorithm on the preprocessed EEG data and selected ten channels with the highest average power. Finally, we classified positive and negative emotion related to valence and arousal using di↵erent classification algorithms (like KNN, RF, Bagging, Extra Tree, AdaBoost and MLP) for the selected EEG channels as well as for all EEG channels. The accuracy reports achieved for the selected channels were impressive; the highest test accuracy 67.58% for Valence was retrieved from the Bagging and Extra Trees classifier while MLP achieved the highest test accuracy result 63.67% for Arousal.