EEG Classification for Multiclass Motor Imagery BCI

摘要：

　　This paper describes the method for classifying multiclass motor imagery EEG signals of brain–computer interfaces (BCIs) according to the phenomena of event-related desynchronization and synchronization (ERD/ERS).The method of one-versus-one common spatial pattern (CSP) for multiclass feature extraction was employed.And we extended two different kinds of classifiers: 1) support vector machines (SVM) based on maximal average decision value;2) k-nearest neighbor (KNN) rule for multiclass classification.In order to testify the performance of each classifier,dataset IIa of BCI Competition IV (2008) which involved nine subjects in a four-class motor imagery (MI) based BCI experiment were used.And the final classification results showed that our extended SVM classification method based on decision value is much better than the majority voting rule,and the extended KNN performed the best.

关键词： Brain-computer interfaces multiclass motor imagery common spatial pattern support vector machine k-nearest neighbor

作者: Chong Liu Hong Wang Zhiguo Lu

作者单位: School of Mechanical Engineering & Automation, Northeastern University, Shenyang 110819

会议类型: 国际会议

会议名称: the 25th Chinese Control and Decision Conference(第25届中国控制与决策会议)

会议地点: 贵阳

会议语种:英文

页码: 4450-4453

在线出版日期: 2013-05-01（万方平台首次上网日期，不代表论文的发表时间）