Emotion recognition based on EEG feature maps through deep learning network

Engineering Science and Technology, an International Journal

Mladen Russo¹  Ante Topic² 

[1] Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, 21000 Split, Croatia;Corresponding author.;

关键词: Brain-computer interface;    Electroencephalogram;    Emotion recognition;    Valence-arousal model;    Deep learning;    Computer-generated holography;   

DOI  :  

来源: DOAJ

【 摘 要 】

Emotion recognition using electroencephalogram (EEG) signals is getting more and more attention in recent years. Since the EEG signals are noisy, non-linear and have non-stationary properties, it is a challenging task to develop an intelligent framework that can provide high accuracy for emotion recognition. In this paper, we propose a new model for emotion recognition that will be based on the creation of feature maps based on the topographic (TOPO-FM) and holographic (HOLO-FM) representation of EEG signal characteristics. Deep learning has been utilized as a feature extractor method on feature maps, and afterward extracted features are fused together for the classification process to recognize different kinds of emotions. The experiments are conducted on the four publicly available emotion datasets: DEAP, SEED, DREAMER, and AMIGOS. We demonstrated the effectiveness of our approaches in comparison with studies where authors used EEG signals that classify human emotions in the two-dimensional space. Experimental results show that the proposed methods can improve the emotion recognition rate on the different size datasets.

【 授权许可】

Unknown