Title :
Classification of Direction perception EEG Based on PCA-SVM
Author :
Jin, Jing ; Wang, Xingyu ; Wang, Bei
Author_Institution :
East China Univ. of Sci. & Technol., Shanghai
Abstract :
In this paper, an experiment was designed to get the electroencephalography (EEG) when people caught the vision of moving to different direction (right, left, front, back). Through Fourier Transform., the feature of the EEG was obtained. Then, the algorithm of principal component analysis (PCA) was used to simplify the feature. Finally, in order to classify the direction perception EEG, it was distinguished by the feature with support vector machine (SVM). Result proved that the classification of direction perception EEG was feasible.
Keywords :
electroencephalography; medical signal processing; principal component analysis; signal classification; support vector machines; Fourier transform; PCA-SVM; direction perception EEG; direction perception classification; principal component analysis; support vector machine; Back; Electrodes; Electroencephalography; Fourier transforms; Frequency; Information science; Principal component analysis; Rhythm; Support vector machine classification; Support vector machines; EEG; Fourier Transform; PCA; SVM;
Conference_Titel :
Natural Computation, 2007. ICNC 2007. Third International Conference on
Conference_Location :
Haikou
Print_ISBN :
978-0-7695-2875-5
DOI :
10.1109/ICNC.2007.298
