Motor Imagery BCI Research Based on Sample Entropy and SVM

Author

Lei Wang ; Guizhi Xu ; Shuo Yang ; Miaomiao Guo ; Weili Yan ; Jiang Wang

Author_Institution

Province-Minist. Joint Key Lab. of Electromagn. Field & Electr. Apparatus Reliability, Hebei Univ. of Technol., Tianjin, China

fYear

2012

fDate

19-21 June 2012

Firstpage

1

Lastpage

4

Abstract

Brain Computer Interface (BCI) is a system that provides an artificial communication between the human brain and the external world. It may give disabled people direct control over a neuro-prosthesis by their intentions that are reflected in their brain signals. In this paper, brain electric field data (EEG) was recorded though 28 electrodes placed on the scalp. According to the fact that EEG is non-stationary and non-linear; a non-linear dynamic method called Sample Entropy (SampEn) was applied to extract the features of EEG. A Support Vector Machine (SVM) classifier was structured for pattern classification. The final results show that SampEn is an effective method to extract the feature of different brain states.

Keywords

brain-computer interfaces; electroencephalography; feature extraction; medical signal processing; prosthetics; support vector machines; EEG; SVM; SVM classifier; SampEn; artificial communication; brain computer interface; brain electric field data; feature extraction; motor imagery BCI research; neuro-prosthesis; pattern classification; sample entropy; support vector machine classifier; Accuracy; Electroencephalography; Entropy; Feature extraction; Support vector machines; Time series analysis; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Electromagnetic Field Problems and Applications (ICEF), 2012 Sixth International Conference on

Conference_Location

Dalian, Liaoning

Print_ISBN

978-1-4673-1333-9

Type

conf

DOI

10.1109/ICEF.2012.6310370

Filename

6310370