摘要
已有研究表明,人格评估可以通过构建基于脑电信号的回归模型实现。已有研究大多使用事件相关电位或功率谱密度特征进行人格评估,所表示的大脑信息局限于单个区域,但有研究发现认知功能更多依赖于脑区间的相互作用。此外,脑电特征可能存在被试间分布差异,会导致训练得到的回归模型在跨被试人格评估中难以取得准确结果。为了获得更精准的跨被试人格评估结果,本研究提出一种结合脑电功能连接特征和领域自适应技术的人格评估方法。本研究收集了45名正常人在不同情绪图片(正、中、负)刺激下的脑电信号,首先计算59个电极间在5个频段上的相干性作为原始特征集。然后使用基于特征的领域自适应方法将相干特征映射至新的特征空间,在新的特征空间里减小训练集和测试集的分布差异,从而减小被试间差异性。最后采用留一法交叉验证的方式,使用转换后的特征集对支持向量回归模型进行训练和测试。实验结果显示,相比已有研究使用的方法,本文提出的方法提高了回归模型性能,能得到更好的人格评估结果。本研究为人格评估提供了一种新的测量方法和手段。
The research shows that personality assessment can be achieved by regression model based on electroencephalogram(EEG). Most of existing researches use event-related potential or power spectral density for personality assessment, which can only represent the brain information of a single region. But some research shows that human cognition is more dependent on the interaction of brain regions. In addition, due to the distribution difference of EEG features among subjects, the trained regression model can not get accurate results of cross subject personality assessment. In order to solve the problem, this research proposes a personality assessment method based on EEG functional connectivity and domain adaption. This research collected EEG data from 45 normal people under different emotional pictures(positive, negative and neutral). Firstly, the coherence of 59 channels in 5 frequency bands was taken as the original feature set. Then the feature-based domain adaptation was used to map the feature to a new feature space. It can reduce the distribution difference between training and test set in the new feature space, so as to reduce the distribution difference between subjects. Finally, the support vector regression model was trained and tested based on the transformed feature set by leave-one-out cross-validation. What’s more, this paper compared the methods used in previous researches. The results showed that the method proposed in this paper improved the performance of regression model and obtained better personality assessment results. This research provides a new method for personality assessment.
作者
许子明
周月莹
温旭云
牛一帆
李子遇
徐西嘉
张道强
邬霞
XU Ziming;ZHOU Yueying;WEN Xuyun;NIU Yifan;LI Ziyu;XU Xijia;ZHANG Daoqiang;WU Xia(College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,P.R.China;MIIT Key Laboratory of Pattern Analysis and Machine Intelligence,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,P.R.China;College of Artificial Intelligence,Beijing Normal University,Beijing 100875,P.R.China;MOE Engineering Research Center for Intelligent Technology and Educational Application,Beijing Normal University,Beijing 100875,P.R.China;Department of Psychiatry,Affiliated Nanjing Brain Hospital,Nanjing Medical University,Nanjing 211106,P.R.China)
出处
《生物医学工程学杂志》
EI
CAS
CSCD
北大核心
2022年第2期257-266,共10页
Journal of Biomedical Engineering
基金
国家自然科学基金项目(62136004,61876082)
国家重点研发计划(2018YFC2001600,2018YFC2001602)
中国人工智能学会-华为MindSpore学术奖励基金
北京师范大学博士研究生跨学科研究基金(BNUXKJC2021)。
关键词
脑电信号
功能连接
跨被试
人格评估
领域自适应
Electroencephalogram signal
Functional connectivity
Cross subject
Personality assessment
Domain adaptation
