A Novel Method of De-noising and Classifying on Mental EEG of Imaging Left-Right Hands Movement

Author

Wu, Shaochun ; Song, Duhong ; Li, Mingdong ; Xu, Lingyu

Author_Institution

Sch. of Comput. Eng. & Sci., Shanghai Univ. Shanghai, Shanghai, China

fYear

2009

fDate

3-5 Aug. 2009

Firstpage

356

Lastpage

359

Abstract

After analyzing the current wavelet threshold de-noising methods and independent component analysis (ICA) methods in EEG, this paper proposed a novel method for EEG de-noising which combines the new threshold de-noising method with ICA method and implicates it to deal with mental EEG of imaging left-right hands movement, and then classifies the signal by support vector machine (SVM). The correct classification rate of 89.93% is achieved by the approach in this paper.

Keywords

biomechanics; electroencephalography; image classification; image denoising; image segmentation; independent component analysis; medical image processing; support vector machines; wavelet transforms; ICA method; image classification; independent component analysis; left-right hands movement EEG imaging; support vector machine; wavelet threshold de-noising method; Electrocardiography; Electroencephalography; Electrooculography; Independent component analysis; Noise level; Noise reduction; Support vector machine classification; Support vector machines; Wavelet coefficients; Wavelet transforms; De-noising; EEG; ICA; Wavelet transform;

fLanguage

English

Publisher

ieee

Conference_Titel

Bioinformatics, Systems Biology and Intelligent Computing, 2009. IJCBS '09. International Joint Conference on

Conference_Location

Shanghai

Print_ISBN

978-0-7695-3739-9

Type

conf

DOI

10.1109/IJCBS.2009.87

Filename

5260635