Institute of Computer Science, University of Tartu Tartu, Estonia.
Institute of Psychology, University of Tartu Tartu, Estonia.
Front Hum Neurosci. 2015 Feb 13;9:63. doi: 10.3389/fnhum.2015.00063. eCollection 2015.
In the present study we asked whether it is possible to decode personality traits from resting state EEG data. EEG was recorded from a large sample of subjects (n = 289) who had answered questionnaires measuring personality trait scores of the five dimensions as well as the 10 subordinate aspects of the Big Five. Machine learning algorithms were used to build a classifier to predict each personality trait from power spectra of the resting state EEG data. The results indicate that the five dimensions as well as their subordinate aspects could not be predicted from the resting state EEG data. Finally, to demonstrate that this result is not due to systematic algorithmic or implementation mistakes the same methods were used to successfully classify whether the subject had eyes open or closed. These results indicate that the extraction of personality traits from the power spectra of resting state EEG is extremely noisy, if possible at all.
在本研究中，我们探讨了从静息态 EEG 数据解码人格特质是否可行。我们对一个大样本（n = 289）的被试进行了 EEG 记录，这些被试填写了人格特质问卷，问卷中包含了大五人格特质的五个维度和十个下属维度的分数。我们使用机器学习算法构建了一个分类器，用于根据静息态 EEG 数据的功率谱预测每个人格特质。结果表明，无法根据静息态 EEG 数据预测五个维度及其下属维度。最后，为了证明这一结果不是由于系统算法或实现错误导致的，我们使用相同的方法成功地对被试的睁眼或闭眼状态进行了分类。这些结果表明，从静息态 EEG 的功率谱中提取人格特质非常困难，即使有可能实现也是如此。
