Classification of left and right hand motor imagery tasks based on EEG frequency component selection
In this paper, a method based on the time-frequency analysis of EEG frequency spectral Fisher-ratio is proposed to pre-select the most relevant movement-related EEG features. Within this method, combining EEG spectral time-frequency distribution with Fisher criterion, the detailed separability information of the frequency components between two classes of EEG patterns in time-frequency plane over C3, C4, Cz are well characterized, which provides a good guide for selecting the most relevant EEG frequency components. According to Fisher-ratio distribution of EEG spectrum by Matching Pursuit (MP) with high frequency resolution, the matched method Morlet wavelet filter is applied to extract the most relevant EEG frequency components. With the optimized EEG features, two classes of EEG patterns during left and right hand motor imagery are discriminated. Here, BCI competition data are analyzed offline and the satisfactory classification results are obtained, which verify the effectiveness of the proposed method in selecting the most relevant EEG spectral components.
Fisher-ratio Matching Pursuit EEG optimization Morlet wavelet BCI
Xiaomei Pei Chongxun Zheng
Institute of Biomedical Engineering of Xian Jiaotong University Key laboratory of Biomedical Information Engineering of Education Ministry Xian, China
国际会议
上海
英文
1888-1891
2008-05-16（万方平台首次上网日期，不代表论文的发表时间）