北京生物医学工程

视觉刺激下基于“是-否”状态的脑电信号分类研究

Classification of electroencephalogrambased on state of ‘Yes-No’ during visual stimuli experiment

作者：李康宁李明钰李萌杜若瑜

单位：南京邮电大学地理与生物信息学院 (南京 210023) 江苏省智慧健康大数据分析与位置服务工程实验室(南京 210023)

分类号：R318.04

出版年·卷·期（页码）：2020·39·3（257-263）

摘要：

目的为了探究脑机接口中脑电信号与判断认知心理活动之间的识别问题，本文在视觉刺激诱发实验设计中采用文字和图片结合的方式进行脑电分类研究以期提高识别率。方法通过设计视觉刺激诱发判断认知脑电实验采集到15名受试者在“是”或“否”状态下的脑电信号，经过预处理和事件相关扰动(event-related spectral dynamics ，ERSP)特征分析，运用经验模式分解（empirical mode decomposition ，EMD）优化共空间模式(common spatial pattern ，CSP)的特征提取算法进行分类识别。首先，利用EMD对预处理后的脑电信号进行有效的固有模态函数(intrinsic mode function，IMF)频段筛选；其次，使用CSP滤波器进行滤波提取特征向量；最后，使用支持向量机(support vector machine ，SVM)进行分类识别，并对测试组进行检验。结果经过EMD-CSP优化滤波后进行SVM分类正确率可达88.97%，相比单独利用CSP进行特征提取下的SVM分类结果提高了约5%。结论EMD-CSP优化滤波方法对判断认知脑电识别的可行性和有效性，为进一步研究脑机接口应用产品的开发提供认知参考依据。

Objective To explore the relationship of cognitive judgement and brain oscillatory activity in communication cognitive task for brain-computer interface (BCI), this paper uses a combination of text and pictures in the design of visual stimulation-induced experiments to conduct EEG classification research in order to improve the recognition rate. Methods The EEG signals of 15 subjects in "yes" or "no" state are collected through the experiment of visual stimulation induced two basic cognitive judgementstates. After preprocessing and event-related spectral dynamics(ERSP) analysis, the feature extraction algorithm of common spatial pattern (CSP) is optimized by empirical mode decomposition (EMD) for classification and recognition. First of all, EMD is used to filter the frequency band of IMFs. Secondly, CSP filter is used to extract feature vector. Finally, support vector machine (SVM) is used for classification and identification, and the test group is tested. Results After optimized filtering by EMD-CSP, the accuracy of SVM classification can reach 88.97%, which is about 5% higher than that of SVM classification using CSP for feature extraction alone. Conclusions The validity of EMD-CSP optimized filtering method for EEG classification provides a reference for further study of "Yes-No" EEG classification and recognition.

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