Study on EEG classification based on multi-task motor imagery.
- Author:
Chong LIU
1
;
Hong WANG
;
Haibin ZHAO
;
Shiyu YAN
Author Information
1. School of Mechanic & Automation, Northeastern University, Shenyang 110819, China. congliu@me.neu.edu.cn
- Publication Type:Journal Article
- MeSH:
Algorithms;
Brain;
physiology;
Electroencephalography;
Humans;
Imagination;
physiology;
Movement;
physiology;
Psychomotor Performance;
Support Vector Machine;
Task Performance and Analysis
- From:
Journal of Biomedical Engineering
2012;29(6):1027-1031
- CountryChina
- Language:Chinese
-
Abstract:
In order to promote the performance of EEG classification based on multi-task motor imagery (MI), we used common spatial pattern (CSP) as the feature extraction method, and we extracted the features under two conditions, with one "One versus One" and the other "One versus Rest". Then, as for the different feature extraction methods, we presented different classification methods based on support vector machine (SVM) according to the different input features. The final classification results showed that the mean Kappa of "One versus One" classification method based on decision value is much higher than that of voting rule, and a little higher than that of "One versus Rest" classification method.
