|Table of Contents|

Classification of LeftRight Hand Motor Imagery Electroencephalogram Signals Based on a Feature Extraction Common Spatial Pattern Algorithm(PDF)

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2014年02期
Page:
55-
Research Field:
Publishing date:

Info

Title:
Classification of LeftRight Hand Motor Imagery Electroencephalogram Signals Based on a Feature Extraction Common Spatial Pattern Algorithm
Author(s):
Pan Saihu1; Li Wenjie1; 2; Zhang Yi1; 2
(1.Faculty of Information Science and Engineering,Changzhou University,Changzhou 213164,China) (2.Changzhou Key Laboratory of Biomedical Information Technology,Changzhou 213164,China)
Keywords:
motor imagery; braincomputer interface; feature extraction; pattern classification
PACS:
R318.04;TP391.4
DOI:
-
Abstract:
Classification of electroencephalogram(EEG)signal is an important issue in braincomputer interface(BCI).Based on the classification of the EEG signals,in this paper,we collect the leftright hand motor imagery EEG data of 7 subjects which are recorded by EGI-64 scalp electrodes placed according to the international 10/20 system.Firstly,the EEG data are denoised with extend InfomaxIndependent Component Analysis(ICA);Secondly,C3 and C4 electrodes features are extracted by using Common Spatial Pattern(CSP);Finally,the average classification rates of Fisher Linear Discriminant Analysis(FLDA),Bayesian,Radial Basis Function(RBF)neural network and BP neural network methods are compared.The classification results show that the average classification rate of neural network is higher than the other two methods,and that the average classification rate of BP neural network can be up to 95.36%,but the other three methods of running velocity is obviously faster than the BP neural network.The results provide a basis for realtime BCI system implementation.

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Last Update: 2014-06-30