Convolutional neural network-based emotion recognition using recursive feature elimination

Minh Tuan Nguyen, Le Anh Dang Tran, Tuan Anh Vu and Duy Nguyen *

Posts and Telecommunications Institute of Technology, Vietnam.
 
Research Article
International Journal of Science and Research Archive, 2024, 13(01), 2494–2501.
Article DOI: 10.30574/ijsra.2024.13.1.1913
Publication history: 
Received on 31 August 2024; revised on 10 October 2024; accepted on 12 October 2024
 
Abstract: 
Emotion detection plays a crucial role in fields such as biomedical applications, smart environments, brain-computer interfaces, communication, security, and safe driving. In this paper, we present a novel approach for detecting emotions using electroencephalogram signals. The method employs convolutional neural network (CNN) as the classifier, which is chosen from a variety of intelligent algorithms. Discrete wavelet transform is used to decompose the signals into four frequency bands including theta, alpha, beta, and gamma. These bands are then utilized for feature extraction. Out of a total of 1920 features, the recursive feature elimination algorithm based on random forest model combining with 5-fold cross-validation and the K-nearest neighbors model, selects the 720 most relevant features. The proposed algorithm is further validated on the selected feature subset using 5-fold cross-validation with CNN on the validation set. The results demonstrate the potential of this algorithm for emotion recognition.
 
Keywords: 
EEG signals; Deep learning; Machine learning; Discrete wavelet transform; DEAP dataset
 
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