基于事件相关电位(ERPs)和机器学习的考试焦虑诊断  

Classification of test-anxious individuals using Event-Related Potentials (ERPs): The effectiveness of machine learning algorithms

作　　者：章文佩 沈群伦 宋锦涛 周仁来[1] ZHANG Wenpei;SHEN Qunlun;SONG Jintao;ZHOU Renlai(Department of Psychology, Nanjing University, Nanjing, 210023, China;Department of Business Administration, School of Business, Anhui University of Technology, Maanshan, 243032, China;Academy of Mathematics and Systems Science, Chinese Academy of Sciences, 100190, China)

机构地区：[1]南京大学心理系,南京210023 [2]安徽工业大学工商管理系,马鞍山243032 [3]中国科学院数学与系统科学研究院,北京100190

出　　处：《心理学报》2019年第10期1116-1127,共12页Acta Psychologica Sinica

基　　金：中央高校基本科研业务费专项资金(14370303);江苏省普通高校学术学位研究生科研创新计划项目(KYZZ16_0010);安徽省高校人文科学研究项目(SK2017A0084)资助

摘　　要：考试焦虑对个体的身心具有严重危害。传统诊断考试焦虑的方法容易受到个体主观态度的影响,从而影响对个体考试焦虑的发现与及早干预。为了克服传统主观问卷对考试焦虑群体诊断的不足,本研究提出脑电神经数据结合机器学习的客观综合诊断方法评估个体的考试焦虑水平。研究采用情绪Stroop范式,结合脑电技术测量个体对考试焦虑者的注意抑制功能,机器学习基于此前提,提取P1, P2, N2, P3和LPP五种事件相关电位(ERP)成分,以卷积神经网络(CNN)为主采用7种常见的机器学习算法对个体考试焦虑程度进行进一步的诊断。结果表明CNN对考试焦虑诊断的准确率达86.5%, F1-score为0.911,显著高于其他6种常见算法。因此采用CNN对脑电信号进行深度学习得出的诊断模型能够有效地对个体的考试焦虑程度进行诊断。Individuals with test anxiety always treat tests/examinations as a potential threat. This cognitive mode impairs these individuals’ cognition, attention and emotions. A traditional method classifying subjects either as high or low on test anxiety (i.e., HTA or LTA, respectively) relies on questionnaire data. Questionnaire data may be unstable due to the subjective nature of participants’ attitudes, implying a reduced classification accuracy. In search for higher levels of (data) stability and classification accuracy a new classification approach is proposed. This new approach overcomes subjective data’s negative impact on classification accuracy by relying on event-related potential (EPR) data (also referred to as ERPs), objective (multivariate, longitudinal) data which adequately capture participants’ reactions to relevant stimuli (over time). However, as ERP data may still be somewhat unstable due to individual differences between participants,(machine) learning algorithms are adopted as their ‘learning’ feature may increase both the stability of ERP data and classification accuracy.

关 键 词：机器学习 考试焦虑 情绪Stroop ERPS 

分 类 号：R395[哲学宗教—心理学]