基于独立分量分析和共同空间模式的脑电特征提取方法

EEG Feature Extraction Based on ICA and CSP Algorithms

针对脑机接口（BCI）中的特征提取问题,提出了独立分量分析（ICA）和共同空间模式（CSP）结合的方法,实现脑电频域特征的有效提取。首先利用ICA预处理得到去噪后的脑电（EEG）信号,并进行8~30 Hz滤波;然后通过CSP将EEG信号分解为空间模式,采用谱分析提取事件相关去同步/事件相关同步特征;最后用支持向量机实现运动想象任务的分类。采用BCI Competition 2008-Graz data set B运动想象脑电数据验证上述方法。结果表明,ICA和CSP结合能有效提高信噪比并提取出明显的特征,是分类识别的有效方法。

This research was aimed at the feature extraction problem in brain computer interface（BCI）.The combination algorithm based on independent component analysis（ICA） and common spatial pattern（CSP） was introduced into this work for exploring frequency domain characteristics from Electroencephalography（EEG）.Firstly,a preprocessing step with ICA was applied to remove artifacts,and EEG was filtered through an 8-30Hz bandpass filter.Secondly,EEG was decomposed into spatial patterns with CSP,which were extracted from two most discriminative populations,and event related desynchronization（ERD）/event related synchronization（ERS） characteristic was extracted with power spectrum analysis.Finally,support vector machine（SVM） was used to classify motor imagery tasks,and good results were obtained.For validation,the motor imagery EEG data provided by BCI Competition 2008-Graz data set B were used,and the results showed that the combination algorithm enhanced the signal-to-noise ratio and extracted discriminative characteristics.It was an effective method for classification recognition.