Electroencephalogram Based Emotion Recognition Using Hybrid Intelligent Method and Discrete Wavelet Transform

Abstract

Electroencephalography-based emotion recognition is essential for brain-computer interface combined with artificial intelligence. This paper proposes a novel algorithm for human emotion detection using a hybrid paradigm of convolutional neural networks and a boosting model. The proposed algorithm employs two subsets of 18 and 14 features extracted from four sub-bands using discrete wavelet transform. These features are identified as the optimal subsets of the most relevant, among 42 original input features extracted from two subsets of 8 and 6 productive channels using a dual genetic algorithm combined with a wise-subject 5-fold cross validation procedure in which the first and second genetic algorithms address the efficient channels and optimal feature subsets. The feature subsets are estimated by differently intelligent models and wise-subject 5-fold cross validation procedure on the validation set. The proposed algorithm produces an accuracy of 70.43%/76.05%, precision of 69.88%/74.57%, recall of 98.70%/99.17%, and F1 score of 81.83%/85.13% for valence/arousal classifications, which suggest that the frontal and left regions of the cortex associate especially to human emotions.