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基于皮尔逊最优电极选择的ADHD患者脑电特征提取及分类研究

  

  1. (1. 常州大学信息科学与工程学院，江苏 常州 213164；
    2. 常州市生物医学信息技术重点实验室，江苏 常州 213164；
    3. 苏州大学附属第三医院脑科学研究中心，江苏 常州 213003)
  • 出版日期:2020-06-30 发布日期:2020-08-18
  • 基金资助:
    江苏省科技厅社会发展项目(BE2018638)；江苏省“333高层次人才培养工程”项目；常州市社会发展项目(CE20195025)；常州大学科研项
    目(ZMF18020322)；江苏省教育厅首批中外合作办学平台联合科研项目；江苏省研究生培养创新计划项目(KYCX20_2552，KYCX20_2559)

Study on the extraction and classification of EEG characteristics in ADHD patients based on Pearson’s optimal electrode selection

  1. (1. Faculty of Information Science & Engineering, Changzhou University, Changzhou Jiangsu 213164, China;
    2. Changzhou Key Laboratory of Biomedical Information Technology, Changzhou Jiangsu 213164, China;
    3. Brain Science Research Center, the Third Affiliated Hospital of Suzhou University, Changzhou Jiangsu 213003, China)
  • Online:2020-06-30 Published:2020-08-18

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摘要： 事件相关电位(ERP)可用于注意缺陷多动障碍儿童(ADHD)和正常儿童的脑电特征
提取与分类。首先，采用赌博任务范式，采集2 类儿童的脑电信号；其次，基于皮尔逊相关系
数算法选择最优电极，并预处理最优电极脑电信号；然后，提取预处理脑电信号的时域特征(均
值、方差、峰值)和频域特征(Theta 波段功率、Alpha 波段功率)；最后，利用传统分类方法支持
向量机(SVM)、自适应增强(AdaBoost)、自举汇聚法(Bagging)、线性判别式分析(LDA)、反向传
播(BP)和组合分类器的分类方法(LDA-SVM，BP-SVM)完成对2 种脑电信号的分类。研究结果
表明，传统方法BP 分类器的分类准确率可达80.52%，组合分类器BP-SVM 的分类准确率可达
88.88%。组合分类方法能提高ADHD 儿童的分类准确率，为基于脑机接口技术的ADHD 神经
反馈康复治疗提供技术支持。

关键词: 事件相关电位, 皮尔逊相关系数, 赌博任务范式, 脑电分类, 脑机接口

Abstract: Event-related potential (ERP) can be used for EEG feature extraction and classification for
children with attention deficit hyperactivity disorder (ADHD) and normal children. Firstly, the EEG
signals of two kinds of children were collected by the gambling task paradigm. Secondly, the optimal
electrode was selected based on the Pearson correlation coefficient algorithm, and the optimal
electrode EEG signal was preprocessed. Then, time domain features (mean, variance, peak) and
frequency domain features (Theta band power, Alpha band power) of pre-processed EEG signals were
extracted. Finally, traditional classification methods (Support Vector Machine (SVM), Adaptive
Boosting (AdaBoost), Bootstrap Aggregating (Bagging), Linear Discriminant Analysis (LDA), Back Propagation (BP) and combined classifier classification methods (LDA-SVM, BP-SVM) were used to
complete the classification of two kinds of EEG signals. The results demonstrate that the
classification accuracy of traditional BP classifier was up to 80.52% and that of the combined
classifier was up to 88.88%. The combined classification method can improve the classification
accuracy for ADHD children and provide technical support for ADHD neurofeedback rehabilitation
treatment based on the BCI technology.

Key words: event-related potential, Pearson correlation coefficient, gambling task paradigm, EEG
classification, brain-computer interface