MMPosE: Movie-induced multi-label positive emotion classification through EEG signals

Du, Xiaobing, Deng, Xiaoming, Qin, Hangyu, Shu, Yezhi, Liu, Fang, Zhao, Guozhen, Lai, Yu-Kun ORCID: https://orcid.org/0000-0002-2094-5680, Ma, Cuixia, Liu, Yong-Jin and Wang, Hongan 2023. MMPosE: Movie-induced multi-label positive emotion classification through EEG signals. IEEE Transactions on Affective Computing 14 (4) , pp. 2925-2938. 10.1109/TAFFC.2022.3221554

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Abstract

Emotional information plays an important role in various multimedia applications. Movies, as a widely available form of multimedia content, can induce multiple positive emotions and stimulate people's pursuit of a better life. Different from negative emotions, positive emotions are highly correlated and difficult to distinguish in the emotional space. Since different positive emotions are often induced simultaneously by movies, traditional single-target or multi-class methods are not suitable for the classification of movie-induced positive emotions. In this paper, we propose TransEEG, a model for multi-label positive emotion classification from a viewer's brain activities when watching emotional movies. The key features of TransEEG include (1) explicitly modeling the spatial correlation and temporal dependencies of multi-channel EEG signals using the Transformer structure based model, which effectively addresses long-distance dependencies, (2) exploiting the label-label correlations to guide the discriminative EEG representation learning, for that we design an Inter-Emotion Mask for guiding the Multi-Head Attention to learn the inter-emotion correlations, and (3) constructing an attention score vector from the representation-label correlation matrix to refine emotion-relevant EEG features. To evaluate the ability of our model for multi-label positive emotion classification, we demonstrate our model on a state-of-the-art positive emotion database CPED. Extensive experimental results show that our proposed method achieves superior performance over the competitive approaches.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	Institute of Electrical and Electronics Engineers
ISSN:	1949-3045
Date of First Compliant Deposit:	19 November 2022
Date of Acceptance:	5 November 2022
Last Modified:	17 Jan 2024 12:30
URI:	https://orca.cardiff.ac.uk/id/eprint/154335

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