Independent Components of EEG Activity Correlating with Emotional State
1
Institute of Innovative Research, Tokyo Institute of Technology, Yokohama, Kanagawa 226-8503, Japan
2
Department of Advanced Neuroimaging, Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Kodaira, Tokyo 187-8551, Japan
3
PRESTO, JST, Kawaguchi, Saitama 332-0012, Japan
4
Neural Information Analysis Laboratories, ATR, Kyoto 619-0288, Japan
*
Author to whom correspondence should be addressed.
Brain Sci. 2020, 10(10), 669; https://doi.org/10.3390/brainsci10100669
Received: 12 August 2020
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Revised: 17 September 2020
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Accepted: 23 September 2020
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Published: 25 September 2020
(This article belongs to the Special Issue Brain Computer Interfaces and Emotional Involvement: Theory, Research, and Applications)
Among brain-computer interface studies, electroencephalography (EEG)-based emotion recognition is receiving attention and some studies have performed regression analyses to recognize small-scale emotional changes; however, effective brain regions in emotion regression analyses have not been identified yet. Accordingly, this study sought to identify neural activities correlating with emotional states in the source space. We employed independent component analysis, followed by a source localization method, to obtain distinct neural activities from EEG signals. After the identification of seven independent component (IC) clusters in a k-means clustering analysis, group-level regression analyses using frequency band power of the ICs were performed based on Russell’s valence–arousal model. As a result, in the regression of the valence level, an IC cluster located in the cuneus predicted both high- and low-valence states and two other IC clusters located in the left precentral gyrus and the precuneus predicted the low-valence state. In the regression of the arousal level, the IC cluster located in the cuneus predicted both high- and low-arousal states and two posterior IC clusters located in the cingulate gyrus and the precuneus predicted the high-arousal state. In this proof-of-concept study, we revealed neural activities correlating with specific emotional states across participants, despite individual differences in emotional processing.
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Keywords:
brain-computer interface (BCI); electroencephalography (EEG); emotion recognition; independent component analysis (ICA); regression
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MDPI and ACS Style
Maruyama, Y.; Ogata, Y.; Martínez-Tejada, L.A.; Koike, Y.; Yoshimura, N. Independent Components of EEG Activity Correlating with Emotional State. Brain Sci. 2020, 10, 669. https://doi.org/10.3390/brainsci10100669
AMA Style
Maruyama Y, Ogata Y, Martínez-Tejada LA, Koike Y, Yoshimura N. Independent Components of EEG Activity Correlating with Emotional State. Brain Sciences. 2020; 10(10):669. https://doi.org/10.3390/brainsci10100669
Chicago/Turabian StyleMaruyama, Yasuhisa, Yousuke Ogata, Laura A. Martínez-Tejada, Yasuharu Koike, and Natsue Yoshimura. 2020. "Independent Components of EEG Activity Correlating with Emotional State" Brain Sciences 10, no. 10: 669. https://doi.org/10.3390/brainsci10100669
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