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EEG-based approach for recognizing human social emotion perception

Zhu, Li and Su, Chongwei and Zhang, Jianhai and Cui, Gaochao and Cichocki, Andrzej and Zhou, Changle and Li, Junhua (2020) 'EEG-based approach for recognizing human social emotion perception.' Advanced Engineering Informatics, 46. ISSN 0954-1810

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Abstract

Social emotion perception plays an important role in our daily social interactions and is involved in the treatments for mental disorders. Hyper-scanning technique enables to measure brain activities simultaneously from two or more persons, which was employed in this study to explore social emotion perception. We analyzed the recorded electroencephalogram (EEG) to explore emotion perception in terms of event related potential (ERP) and phase synchronization, and classified emotion categories based on convolutional neural network (CNN). The results showed that (1) ERP was significantly different among four emotion categories (i.e., anger, disgust, neutral, and happy), but there was no significant difference for ERP in the comparison of rating orders (the order of rating actions of the paired participants); (2) the intra-brain phase lag index (PLI) was higher than the inter-brain PLI but its number of connections exhibiting significant difference was less in all typical frequency bands (from delta to gamma); (3) the emotion classification accuracy of inter-PLI-Conv outperformed that of intra-PLI-Conv for all cases of using each frequency band (five frequency bands totally). In particular, the classification accuracies averaged across all participants in the alpha band were 65.55% and 50.77% (much higher than the chance level) for the inter-PLI-Conv and intra-PLI-Conv, respectively. According to our results, the emotion category of happiness can be classified with a higher performance compared to the other categories.

Item Type: Article
Additional Information: Deep learning
Uncontrolled Keywords: Hyper-scanning, EEG, Emotion, Phase lag index
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 13 Nov 2020 15:22
Last Modified: 26 Oct 2021 01:00
URI: http://repository.essex.ac.uk/id/eprint/29088

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