Paper Info
Title
An Improved Support Vector Machine Classifier for EEG-Based Motor Imagery Classification
Abstract
Electroencephalogram (EEG) recordings during motor imagery tasks are often used as input signals for brain-computer interfaces (BCIs). We analyze the EEG signals with Daubechies order 4 (db4) wavelets in 10 Hz and 21Hz at C3 channel, and in 10 Hz and 20 Hz at C4 channel, for these frequencies are prominent in discrimination of left and right motor imagery tasks according to EEG frequency spectral. We apply the improved support vector machines (SVMs) for classifying motor imagery tasks. First, a SVM is trained on all the training samples, then removes the support vectors which contribute less to the decision function from the training samples, finally the SVM is re-trained on the remaining samples. The classification error rate of the presented approach was as low as 9.29 % and the mutual information could be 0.7 above based on the Graz BCI 2003 data set.
Year
DOI
Venue
2009
10.1007/978-3-642-01510-6_31
ISNN (2)
Keywords
Field
DocType
right motor imagery task,classifying motor imagery task,vector machine classifier,eeg-based motor imagery classification,training sample,c4 channel,c3 channel,improved support vector machine,support vector,improved support,motor imagery task,eeg signal,eeg frequency spectral,mutual information,support vector machine,motor imagery,eeg,svms,continuous wavelet transform,brain computer interface
Computer science,Brain–computer interface,Artificial intelligence,Electroencephalography,Wavelet,Pattern recognition,Word error rate,Support vector machine,Communication channel,Speech recognition,Mutual information,Machine learning,Motor imagery
Conference
Volume
ISSN
Citations 
5552
0302-9743
1
PageRank 
References 
Authors
0.43
3
5
Name
Order
Citations
PageRank
Hui Zhou110.43
Qi Xu221.15
Yongji Wang360675.34
Jian Huang42608200.50
Jun Wu5546.16