Independent component analysis for spatial filtering and feature extraction in a four-task brain-computer interface

摘要：

Independent component analysis (ICA) is one statistical method closely related to the method called blind signal separation. In this paper, three typical ICA algorithms FastICA, Infomax and SOBI are utilized for spatial filtering and feature extraction in a four-task brain-computer interface (BCI) by decomposing EEG signals into independent sources. These algorithms are applied to five data sets recorded during motor imagery based BCI experiment and compared with well known algorithm common spatial pattern (CSP) in terms of classification performance. Averaged classification accuracies over the five data sets achieved by FastICA and Informax are better than or equal to that yielded by CSP algorithm, verifying the usefulness and feasibility of ICA methods for multi-task BCI application.

关键词： brain-computer interface feature extraction spatial filtering independent component analysis

作者: Qingguo Wei Yuhui Ma Zongwu Lu

作者单位: Department of Electronic Engineering,Nanchang University,Nanchang 330031,China

会议类型: 国际会议

会议名称: 2010 Second International Conference on Intelligent Human-Machine Systems and Cybernetics(第二届智能人机系统与控制论国际学术会议 IHMSC 2010)

会议地点: 南京

会议语种:英文

页码: 490-493

在线出版日期: 2010-08-26（万方平台首次上网日期，不代表论文的发表时间）