Multi-Task CNN model for emotion recognition from EEG Brain maps
Evgenii Rudakov, Lou Laurent, Valentin Cousin, Ahmed Roshdi, Régis Fournier, Amine Naït‐Ali, Taha Beyrouthy, Samer Al Kork
Abstract
Emotion identification plays a vital role in human interactions. For this purpose, Computer-vision methods for automatic emotion recognition is nowadays a widely studied topic. One of the most studied approaches for automatic emotion recognition is processing multi-channel Electroencephalogram signals (EEG). This paper presents a new model for emotion recognition using brain maps as input and providing emotion states in terms of arousal and valence as output. Brain maps are a spatial representation of features extracted from EEG signals. The proposed model, called Multi-Task Convolutional Neural Network (MT-CNN), is fed with stacked brain maps of four different waves of different frequency bands: alpha, beta, gamma and theta, using differential entropy and power spectra density and considering observation windows of 0.5s. This model is trained and tested on the DEAP dataset, a well-known dataset for comparison purposes. This work shows that the MT-CNN nerforms better than other methods.