Emotion recognition based on EEG feature maps through deep learning network

• Published in: Engineering science and technology, an international journal Vol. 24; no. 6; pp. 1442 - 1454
• Main Authors: Topic, Ante, Russo, Mladen
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2021; Elsevier
• Subjects: Brain-computer interface; Computer-generated holography; Deep learning; Electroencephalogram; Emotion recognition; Valence-arousal model; Brain-computer interface; Deep learning; Emotion recognition; Electroencephalogram; Valence-arousal model; Computer-generated holography
• ISSN: 2215-0986; 2215-0986
• DOI: 10.1016/j.jestch.2021.03.012

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.