Spectral Analysis of Brain Function Network for the Classification of Motor Imagery Tasks
In order to deal with the classification for multiclass motor imagery(MI) tasks, a novel approach was presented in this paper. It is different from classical methods which classified the MI task with time-frequency analysis on EEG signals. It employs the brain function network(BFN) as a new characteristic to describe MI tasks. The BFN enlarges the features with respect to traditional timefrequency methods. Unlike analysis of statistical parameters of network such as average clustering coefficient (C) and the average pathlength (L), the proposed method employed spectral decomposition performing on BFNs, and considered the eigenvalue vector of threshold matrix as features for classification by SVM. Hence, it is speedy enough to meet the requirement of real-time in BCI-based application systems. The result of experiment demonstrates that proposed method can achieve satisfied accuracy of classification on multi-class MI tasks.
motor imagery brain function network classification BCI
Wanzeng Kong Xinwei Guo Xinxin Zhao Darning Wei Sanqing Hu Guojun Dai Giovanni Vecchiato Fabio Babiloni
College of Computer Science Hangzhou Dianzi University Hangzhou, China Dept of Physiology and Pharmacology Rome University Rome, Italy
国际会议
上海
英文
852-855
2011-10-15（万方平台首次上网日期，不代表论文的发表时间）