Parameter and feature selection in decision trees for the classification of musical impressions from EEG records

Abstract

Reliable classification of different emotions is an important issue for emotional interaction between humans and computers. Therefore, this study aims at assessing the performance of decision trees in classifying musical impressions from EEG records of 20 subjects, who listened to songs in different music styles. First, features extracted from the clean EEG data used to train the classifier, where different feature combinations and parameter settings are considered. Next, the impact of various hyperparameter values on the classification accuracy is examined and the relevant feature combination is specified. According to the results, an accuracy rate of 76,12% is achieved, when all time domain features are included in the classification.