Correlation Indices of Electroencephalogram-Based Relative Powers during Human Emotion Processing

N. Al-Qazzaz, Mohannad K. Sabir, K. Grammer
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引用次数: 7

Abstract

The present study sought to employ audio-visual video clip stimuli to explore the electroencephalography-based (EEG) correlation between various emotional states. To that end, seven short video clips were shown to ten volunteer participants without health conditions whilst emotional EEG data were captured. The method of independent component analysis and wavelets (AICA-WT) was adopted for screening the extracted data. The correlation indices were computed based on spectral features employing the relative powers (RP) of delta (ΔRP), theta (&thetas;RP), alpha (αRP), beta (βRP), and gamma (γRP). The next step was calculation of Pearson's correlation between the of the neutral state and the of the six fundamental emotional states (i.e. anger, anxiety, disgust, happiness, sadness and surprise) of every EEG channel for different brain areas (i.e. frontal, temporal, parietal and occipital scalp). According to the findings obtained, the correlation of brain activity and emotional states among the brain areas observable in healthy EEG data can be investigated based on the relevant indices afforded by the new denoising method alongside EEG-based correlation analysis of the RP.
基于脑电图的人类情绪处理相对功率的相关指标
本研究试图利用视听视频片段刺激来探讨不同情绪状态之间的脑电图相关性。为此,研究人员向10名没有健康状况的志愿者播放了7个短视频片段,同时捕捉了他们的情绪脑电图数据。采用独立分量分析和小波(AICA-WT)方法对提取的数据进行筛选。利用delta (ΔRP)、theta (θRP)、alpha (αRP)、beta (βRP)和gamma (γRP)的相对幂(RP),根据光谱特征计算相关指标。下一步是计算不同脑区(即额叶、颞叶、顶叶和枕叶头皮)的每个脑电图通道的中性状态与六种基本情绪状态(即愤怒、焦虑、厌恶、快乐、悲伤和惊讶)之间的Pearson相关性。根据所获得的结果,基于新的去噪方法提供的相关指标,结合基于脑电图的RP相关性分析,可以研究健康脑电图数据中观察到的脑区之间的脑活动与情绪状态的相关性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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