Spatiotemporal Emotion Recognition using Deep CNN Based on EEG during Music Listening
This work addresses emotion recognition for EEG analysis, but it is incremental as it builds on existing CNN and SVM methods with minor variations in feature extraction.
The study tackled emotion recognition from EEG during music listening by investigating how spatiotemporal feature extraction in CNNs, particularly varying window sizes and electrode orders, affects performance. Results showed that temporal information significantly impacts recognition, with CNN outperforming SVM in leave-one-subject-out cross-validation.
Emotion recognition based on EEG has become an active research area. As one of the machine learning models, CNN has been utilized to solve diverse problems including issues in this domain. In this work, a study of CNN and its spatiotemporal feature extraction has been conducted in order to explore capabilities of the model in varied window sizes and electrode orders. Our investigation was conducted in subject-independent fashion. Results have shown that temporal information in distinct window sizes significantly affects recognition performance in both 10-fold and leave-one-subject-out cross validation. Spatial information from varying electrode order has modicum effect on classification. SVM classifier depending on spatiotemporal knowledge on the same dataset was previously employed and compared to these empirical results. Even though CNN and SVM have a homologous trend in window size effect, CNN outperformed SVM using leave-one-subject-out cross validation. This could be caused by different extracted features in the elicitation process.