Mental task classification for brain computer interface application
Mental task classification for brain computer interface application
复制标题
脑机接口应用的心理任务分类
DOI：
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发表时间：
2007
期刊：
影响因子：
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通讯作者：
Xiaopei Wu
中科院分区：
文献类型：
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作者：
Xiaopei Wu
脑电信号（EEG）是大脑底层活动的重要信息源，基于EEG的人机交互是一种新型的人机交互方式，本文采用独立分量分析（伊卡）对不同心理任务的EEG信号进行预处理，提取AR模型系数作为特征向量，并对不同心理任务的EEG信号进行特征提取分析和实验结果表明，该方法能够获得较高的分类正确率
Electroencephalogram(EEG) signal is an important information source of underlying brain processes.The communication based on EEG between human brain and computer is a new modality of human-computer interaction.In this paper,EEG signal of different mental tasks is preprocessed by Independent Component Analysis(ICA),AR model coefficient is extracted as feature vector,and classify the mental tasks based on BP neural network.According to the analysis and experiment results,the method can get high correct rate of classification