Classifying EEG signals based HMM-AR
Whether movement or rest in Electroencephalogram (EEG) plays an important role in the domain of brain computer interface (BCI). Hidden Markov model (HMM)-AR might be a useful tool in EEG pattern classification since EEG data is a multivariate time series data which contains noise and artifacts. The methods are presented for EEG pattern classification which jointly employ Laplacian filter, ICA transform and HMM. Our hybrid method is confirmed through the classification of EEG that is recorded during the imagination of a left or right hand movement. The algorithm for cue movements determination has been designed resulting in detecting the movements within one second interval.
Brain-Computer Interface(BCI) Hidden Markov AR Models(HMM-AR) Independent Component Analysis(ICA) Electroencephalogram(EEG)
Tang Yan Tang Jingtian Gong Andong Wang Wei
Institute of Info-Physics Engineering Central South University Changsha, China Department of Radiology, Third Hospital Central South University Changsha, China
国际会议
上海
英文
2111-2114
2008-05-16（万方平台首次上网日期，不代表论文的发表时间）