基于共空间模式和神经元网络的脑-机接口信号的识别

提出了一种基于共空间模式和LVQ神经元网络对不同意识的脑电信号进行分类的方法.脑电信号是通过电极在头皮表面采集的脑-机接口的控制信号,提取脑电信号特征并对其进行分类,组成不依赖于正常的由外围神经和肌肉组成的输出通路的通讯系统.首先利用小波包分解对原始脑电信号进行预处理,对分解后特定小波包子带的脑电信号进行共空间模式分解,提取最优的特征;然后利用LVQ网络对不同意识任务特征进行分类,实验结果表明,该方法取得了92.7%的平均分类识别率,已经达到脑-机接口实际应用的标准.

Recognition Based on Common Spatial Patterns and ANN for Brain-Computer Interface Signal

Classifies the EEG signals of different ideas based on the common spatial patterns(CSP) and LVQ-ANN. EEG is the control signals of brain-computer interface(BCI),which are collected from one's scalp by electrodes to extract and classify the EEG features so as to form a communication system that does not depend on the brain's normal output channels of peripheral nerves and muscles. With the original EEG signals preprocessed by wavelet packet decomposition,the CSP is introduced to decompose further the EEG sig...