Classification for different mental tasks based on EEG signals

Computer Engineering and Applications ›› 2007, Vol. 43 ›› Issue (3): 201-201.

• 工程与应用 • Previous Articles Next Articles

Classification for different mental tasks based on EEG signals

Received:2006-05-31 Revised:1900-01-01 Online:2007-01-21 Published:2007-01-21

脑机接口应用中的思维任务分类

胡人君李坤吴小培

安徽大学安徽大学计算机科学与信息工程学院

通讯作者: 胡人君

Abstract

Abstract: 【Abstract】 Electroencephalogram, or EEG, signals are an important source of information for study of underlying brain processes. The communication based on EEG between human brain and computer is a new modality of human-computer interaction. In this article, EEG signals of different mental tasks are preprocessed using Independent Component Anlysis(ICA), AR model is used to extract the feature, and Back-Propagation(BP)network as the classifier. The result prove that method can improve the accuracy of classifying different mental tasks and get better discrimination accuracy.

Key words: EEG, Independent Component Anlysis (ICA), AR model, BP network

摘要： 【摘要】脑电信号（EEG）是研究脑活动的一种重要信息来源，基于EEG的人与计算机的通信成为一种新的人机接口方式。本文对于五种不同心理作业的思维脑电信号运用独立分量分析（ICA）进行预处理后，然后采用6阶的AR模型提取特征，最后应用BP神经网络对AR系数特征进行训练和分类。试验表明，应用此方法可以达到和好的分类效果，提高了脑电思维作业的准确度。

关键词: 脑电信号, 独立分量分析, 自回归模型, BP神经网络

胡人君李坤吴小培. 脑机接口应用中的思维任务分类[J]. 计算机工程与应用, 2007, 43(3): 201-201.

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Classification for different mental tasks based on EEG signals

脑机接口应用中的思维任务分类

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