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Decoding personality trait measures from resting EEG: An exploratory report.
Cortex ( IF 3.6 ) Pub Date : 2020-06-13 , DOI: 10.1016/j.cortex.2020.05.013
Hayley K Jach 1 , Daniel Feuerriegel 1 , Luke D Smillie 1
Affiliation  

Can personality be predicted from oscillatory patterns produced by the brain at rest? To date, relatively few studies using electroencephalography (EEG) have yielded consistent relations between personality trait measures and spectral power. Thus, new exploratory research may help develop targeted hypotheses about how neural processes associated with EEG activity may relate to personality differences. We used multivariate pattern analysis to decode personality scores (i.e., Big Five traits) from resting EEG frequency power spectra. Up to 8 minutes of EEG data was recorded per participant prior to completing an unrelated task (N = 168, Mage = 23.51, 57% female) and, in a subset of participants, after task completion (N = 96, Mage = 23.22, 52% female). In each recording, participants alternated between open and closed eyes. Linear support vector regression with 10-fold cross validation was performed using the power from 62 scalp electrodes within 1 Hz frequency bins from 1 to 30 Hz. One Big Five trait, agreeableness, could be decoded from EEG power ranging from 8 to 19 Hz, and this was consistent across all four recording periods. Neuroticism was decodable using data within the 3–6 Hz range, albeit less consistently. Posterior alpha power negatively correlated with agreeableness, whereas parietal beta power positively correlated with agreeableness. We suggest methods to draw from our results and develop targeted future hypotheses, such as linking to individual alpha frequency and incorporating self-reported emotional states. Our open dataset can be harnessed to reproduce results or investigate new research questions concerning the biological basis of personality.



中文翻译:

从静止的脑电图中解码人格特质的测量:一项探索性报告。

可以通过大脑静止时产生的振荡模式来预测人格吗?迄今为止,相对较少的使用脑电图(EEG)的研究已经得出了人格特质测度与频谱功率之间的一致关系。因此,新的探索性研究可能有助于发展针对性的假设,即与EEG活动相关的神经过程可能与人格差异有关。我们使用多元模式分析来从静息EEG频率功率谱中解码人格分数(即“大五”特质)。在完成一项无关任务之前,每位参与者最多记录8分钟的EEG数据(N  = 168,M年龄 = 23.51,57%是女性),在部分参与者中,任务完成后(N  = 96,M年龄 = 23.22,52%为女性)。在每个记录中,参与者都在睁开和闭合的眼睛之间交替。使用来自1到30 Hz的1 Hz频率范围内的62个头皮电极的功率,执行具有10倍交叉验证的线性支持向量回归。可以从8到19 Hz的EEG功率中解码出“五大”特性,即合意性,这在所有四个记录周期中都是一致的。使用3-6 Hz范围内的数据可以对神经质进行解码,尽管一致性较差。后验α能力与宜人性呈负相关,而顶叶β能力与宜人性呈正相关。我们建议从我们的结果中吸取教训并制定有针对性的未来假设的方法,例如链接到各个alpha频率并结合自我报告的情绪状态。

更新日期:2020-07-10
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