Maruyama Yasuhisa, Ogata Yousuke, Martínez-Tejada Laura A, Koike Yasuharu, Yoshimura Natsue
Institute of Innovative Research, Tokyo Institute of Technology, Yokohama, Kanagawa 226-8503, Japan.
Department of Advanced Neuroimaging, Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Kodaira, Tokyo 187-8551, Japan.
Brain Sci. 2020 Sep 25;10(10):669. doi: 10.3390/brainsci10100669.
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.
在脑机接口研究中，基于脑电图（EEG）的情绪识别受到关注，一些研究已进行回归分析以识别小规模的情绪变化；然而，情绪回归分析中的有效脑区尚未确定。因此，本研究旨在识别源空间中与情绪状态相关的神经活动。我们采用独立成分分析，随后使用源定位方法，从EEG信号中获取不同的神经活动。在k均值聚类分析中识别出七个独立成分（IC）簇后，基于罗素效价-唤醒模型，使用IC的频段功率进行组水平的回归分析。结果，在效价水平的回归中，位于楔叶的一个IC簇预测了高、低价态，位于左中央前回和楔前叶的另外两个IC簇预测了低价态。在唤醒水平的回归中，位于楔叶的IC簇预测了高、低唤醒状态，位于扣带回和楔前叶的两个后部IC簇预测了高唤醒状态。在这项概念验证研究中，我们揭示了尽管参与者在情绪处理上存在个体差异，但与特定情绪状态相关的神经活动在参与者之间具有一致性。
