基于近似熵的思维脑电信号分类研究

Classification of EEG Signal Based on Approximate Entropy

采用近似熵 (approximateentropy ,Apen)作为脑电信号的特征参数对不同思维作业脑电信号进行了分类研究 ,并对近似熵算法中参数的选择以及互近似熵在思维脑电分类的应用进行了讨探。研究结果表明 ,近似熵特征在思维作业脑电信号的分类中取得了较好的应用效果。近似熵作为EEG的信号特征为提高思维作业脑电信号的分类正确率提供了一种新的途径 ,在基于思维作业BCI的应用中具有重要的实用价值。

Approximate entropy(Apen) was applied as the feature of EEG signal to classify different mental tasks, and investigate how to select the parameters,how to apply cross apen to classify different mental tasks. The results showed that the classification accuracy of Apen had better classification effects. Apen can be used as a new method to classify different mental tasks to promote the accuracy of classification and it show the certain practical value in mental task BCI system.