Sharan Roneel V, Berkovsky Shlomo, Taib Ronnie, Koprinska Irena, Li Jingjie
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:62-65. doi: 10.1109/EMBC44109.2020.9176108.
Affective personality traits have been associated with a risk of developing mental and cognitive disorders and can be informative for early detection and management of such disorders. However, conventional personality trait detection is often biased and unreliable, as it depends on the honesty of the subjects when filling out the lengthy questionnaires. In this paper, we propose a method for objective detection of personality traits using physiological signals. Subjects are shown affective images and videos to evoke a range of emotions. The electrical activity of the brain is captured using EEG during this process and the multi-channel EEG data is processed to compute the inter-hemispheric asynchrony of the brainwaves. The most discriminative features are selected and then used to build a machine learning classifier, which is trained to predict 16 personality traits. Our results show high predictive accuracy for both image and video stimuli individually, and an improvement when the two stimuli are combined, achieving a 95.49% accuracy. Most of the selected discriminative features were found to be extracted from the alpha frequency band. Our work shows that personality traits can be accurately detected with EEG data, suggesting possible use in practical applications for early detection of mental and cognitive disorders.
情感性人格特质与发展为精神和认知障碍的风险相关，并且对于此类障碍的早期检测和管理具有参考价值。然而，传统的人格特质检测往往存在偏差且不可靠，因为它依赖于受试者在填写冗长问卷时的诚实度。在本文中，我们提出了一种利用生理信号客观检测人格特质的方法。向受试者展示情感性图像和视频以唤起一系列情绪。在此过程中使用脑电图（EEG）记录大脑的电活动，并对多通道EEG数据进行处理以计算脑电波的半球间异步性。选择最具区分性的特征，然后用于构建机器学习分类器，该分类器经过训练以预测16种人格特质。我们的结果表明，单独针对图像和视频刺激都具有较高的预测准确性，并且当两种刺激结合使用时准确性有所提高，达到了95.49%。发现大多数选定的区分性特征是从阿尔法频段提取的。我们的工作表明，可以通过EEG数据准确检测人格特质，这表明在精神和认知障碍的早期检测实际应用中可能具有用途。
