Shen Yi-Wei, Lin Yuan-Pin
Institute of Medical Science and Technology, National Sun Yat-sen University, Kaohsiung, Taiwan.
Front Hum Neurosci. 2019 Oct 30;13:366. doi: 10.3389/fnhum.2019.00366. eCollection 2019.
Electroencephalogram (EEG)-based affective brain-computer interfaces (aBCIs) have been attracting ever-growing interest and research resources. Whereas most previous neuroscience studies have focused on single-day/-session recording and sensor-level analysis, less effort has been invested in assessing the fundamental nature of non-stationary EEG oscillations underlying emotional responses across days and individuals. This work thus aimed to use a data-driven blind source separation method, i.e., independent component analysis (ICA), to derive emotion-relevant spatio-spectral EEG source oscillations and assess the extent of non-stationarity. To this end, this work conducted an 8-day music-listening experiment (i.e., roughly interspaced over 2 months) and recorded whole-scalp 30-ch EEG data from 10 subjects. Given the large size of the data (i.e., from 80 sessions), results indicated that EEG non-stationarity was clearly revealed in the numbers and locations of brain sources of interest as well as their spectral modulation to the emotional responses. Less than half of subjects (two to four) showed the same relatively day-stationary (source reproducibility >6 days) spatio-spectral tendency towards one of the binary valence and arousal states. This work substantially advances the previous work by exploiting intra- and inter-individual EEG variability in an ecological multiday scenario. Such EEG non-stationarity may inevitably present a great challenge for the development of an accurate, robust, and generalized emotion-classification model.
基于脑电图（EEG）的情感脑机接口（aBCIs）一直吸引着越来越多的关注和研究资源。然而，此前大多数神经科学研究都集中在单日/单次记录和传感器层面的分析上，在评估跨天和个体的情绪反应背后非平稳脑电振荡的基本性质方面投入的精力较少。因此，这项工作旨在使用一种数据驱动的盲源分离方法，即独立成分分析（ICA），来推导与情绪相关的时空频谱脑电源振荡，并评估非平稳性的程度。为此，这项工作进行了为期8天的音乐聆听实验（即大致间隔2个月），并记录了10名受试者的全脑30通道脑电数据。鉴于数据量较大（即来自80次记录），结果表明，在感兴趣的脑源数量和位置及其对情绪反应的频谱调制方面，脑电非平稳性得到了清晰揭示。不到一半的受试者（两到四名）对二元效价和唤醒状态之一表现出相同的相对日平稳（源再现性>6天）时空频谱趋势。这项工作通过在生态多日场景中利用个体内和个体间的脑电变异性，极大地推进了此前的工作。这种脑电非平稳性可能不可避免地给准确、稳健和通用的情绪分类模型的开发带来巨大挑战。
