Abstract
Emotion recognition from brain signals is an emerging area of interest in the scientific community. We used EEG signals to classify emotional events on different combinations of valence(V), arousal(A) and dominance(D) dimensions and compared their results. DENS data is used for this purpose which is primarily recorded on the Indian population. STFT is used for feature extraction and used in the classification model consisting of CNN-GRU hybrid layers. Two classification models were evaluated to classify emotional feelings in valence-arousal-dominance space (eight classes) and valence-arousal space (four classes). The results show that VAD space’s accuracy is 97.50% and VA space is 96.93%. We conclude that having precise information about emotional feelings improves the classification accuracy in comparison to long-duration EEG signals which might be contaminated by mind-wandering. In addition, our results suggest the importance of considering the dominance dimension during the emotion classification.
M. Asif, M. T. Vinodbhai, S. Mishra, A. Gupta—These authors contributed equally to this work and author sequence is random.
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Asif, M., Vinodbhai, M.T., Mishra, S., Gupta, A., Tiwary, U.S. (2023). Emotion Recognition in VAD Space During Emotional Events Using CNN-GRU Hybrid Model on EEG Signals. In: Zaynidinov, H., Singh, M., Tiwary, U.S., Singh, D. (eds) Intelligent Human Computer Interaction. IHCI 2022. Lecture Notes in Computer Science, vol 13741. Springer, Cham. https://doi.org/10.1007/978-3-031-27199-1_8
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