Sensitive Trans Formation and Multi-Level Spatiotemporal Awareness Based Eeg Emotion Recognition Model
DOI: 10.23977/acss.2022.060404 | Downloads: 20 | Views: 732
Author(s)
Wenting Xu 1, Shuhua Liu 2, Xiangru Hou 1, Xin Yin 3
Affiliation(s)
1 Information Engineering Department, Heilongjiang International University, Harbin, China
2 School of Information Science and Technology, Northeast Normal University, Changchun, China
3 School of Computer Science, Beijing Institute of Technology, Beijing, China
Corresponding Author
Xin YinABSTRACT
Electroencephalography (EEG) emotion recognition has important research value in the fields of medical and criminal investigation, so in recent years, deep learning methods have been widely used in the field of EEG emotion recognition. Generally, the spatial and temporal features of EEG signals can reflect the spatial information of EEG in different brain regions and the long-term features of time-related continuous EEG signals. However, the problem of obtaining accurate spatial and temporal features of sequence signals has been neglected in previous studies. In addition, the spatial information transformation of electrode points on brain regions is not accurate enough. To address these issues, we propose a sensitive transformation and multi-level spatiotemporal awareness based EEG emotion recognition model. Through this method, accurate spatial information and more comprehensive EEG spatiotemporal features can be obtained. The evaluation results of SEED dataset show that the proposed approach improves on the state-of-the-art in EEG emotion recognition. The accuracy rates of subject-dependent and subject-independent emotion recognition are 98.49% and 97.95%, which exceeds the best previous accuracy by 1.18%.
KEYWORDS
Emotion Recognition, EEG, Sensitive Transformation, Spatiotemporal AwarenessCITE THIS PAPER
Wenting Xu, Shuhua Liu, Xiangru Hou, Xin Yin, Sensitive Trans Formation and Multi-Level Spatiotemporal Awareness Based Eeg Emotion Recognition Model. Advances in Computer, Signals and Systems (2022) Vol. 6: 31-41. DOI: http://dx.doi.org/10.23977/acss.2022.060404.
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