摘要
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.
出处
《生物医学工程学杂志》
EI
CAS
CSCD
北大核心
2010年第6期1370-1374,共5页
Journal of Biomedical Engineering
基金
上海高校选拔培养优秀青年教师科研专项基金资助项目(5108508001)
上海医疗器械高等专科学校引进人才科研启动资助项目(A07250902B)
关键词
脑机接口
运动想象
独立分量分析
共同空间模式
Brain computer interface(BCI)
Motor imagery
Independent component analysis(ICA)
Common spatial pattern(CSP)