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阳性分类优势:基于脑振荡追踪时间进程

Positive Classification Advantage: Tracing the Time Course Based on Brain Oscillation.

作者信息

Yan Tianyi, Dong Xiaonan, Mu Nan, Liu Tiantian, Chen Duanduan, Deng Li, Wang Changming, Zhao Lun

机构信息

School of Life Science, Beijing Institute of Technology, Beijing, China.

Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications, Beijing, China.

出版信息

Front Hum Neurosci. 2018 Jan 11;11:659. doi: 10.3389/fnhum.2017.00659. eCollection 2017.

Abstract

The present study aimed to explore the modulation of frequency bands (alpha, beta, theta) underlying the positive facial expressions classification advantage within different post-stimulus time intervals (100-200 ms, 200-300 ms, 300-400 ms). For this purpose, we recorded electroencephalogram (EEG) activity during an emotion discrimination task for happy, sad and neutral faces. The correlation between the non-phase-locked power of frequency bands and reaction times (RTs) was assessed. The results revealed that beta played a major role in positive classification advantage (PCA) within the 100-200 and 300-400 ms intervals, whereas theta was important within the 200-300 ms interval. We propose that the beta band modulated the neutral and emotional face classification process, and that the theta band modulated for happy and sad face classification.

摘要

本研究旨在探讨在不同的刺激后时间间隔(100 - 200毫秒、200 - 300毫秒、300 - 400毫秒)内,积极面部表情分类优势背后的频段(阿尔法、贝塔、西塔)调制情况。为此,我们在对开心、悲伤和中性面孔进行情绪辨别任务期间记录了脑电图(EEG)活动。评估了频段的非锁相功率与反应时间(RTs)之间的相关性。结果显示,在100 - 200毫秒和300 - 400毫秒的时间间隔内,贝塔在积极分类优势(PCA)中起主要作用,而在200 - 300毫秒的时间间隔内,西塔很重要。我们提出,贝塔频段调制了中性和情绪面孔的分类过程,而西塔频段调制了开心和悲伤面孔的分类。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/26b1/5768652/2a1b8406ac55/fnhum-11-00659-g0001.jpg

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