%0 Journal Article
%T Research on Heuristic Feature Extraction and Classification of EEG Signal Based on BCI Data Set
%A Lijuan Duan
%A Qi Zhang
%A Zhen Yang
%A Jun Miao
%J Research Journal of Applied Sciences, Engineering and Technology
%D 2013
%I Maxwell Science Publication
%X In this study, an EEG signal classification framework was proposed. The framework contained three feature extraction methods refer to optimization strategy. Firstly, we selected optimal electrodes based on the single electrode classification performance and combined all the optimal electrodesĄŻ data as the feature. Then, we discussed the contribution of each time span of EEG signals for each electrode and joined all the optimal time spansĄŻ data together to be used for classifying. In addition, we further selected useful information from original data based on genetic algorithm. Finally, the performances were evaluated by Bayes and SVM classifiers on BCI 2003 Competition data set Ia. And the accuracy of genetic algorithm has reached 91.81%. The experimental results show that our methods offer the better performance for reliable classification of the EEG signal.
%K Brain Computer Interface (BCI)
%K Electroencephalogram (EEG)
%K feature extraction
%K genetic algorithm
%U http://maxwellsci.com/jp/abstract.php?jid=RJASET&no=255&abs=46