DocumentCode :
1310417
Title :
Classification of Mental Task From EEG Signals Using Immune Feature Weighted Support Vector Machines
Author :
Guo, Lei ; Wu, Youxi ; Zhao, Lei ; Cao, Ting ; Yan, Weili ; Shen, Xueqin
Author_Institution :
Province-Minist. Joint Key Lab. of Electromagn. Field & Electr. Apparatus Reliability, Hebei Univ. of Technol., Tianjin, China
Volume :
47
Issue :
5
fYear :
2011
fDate :
5/1/2011 12:00:00 AM
Firstpage :
866
Lastpage :
869
Abstract :
The classification of mental tasks is one of key issues of EEG-based brain computer interface (BCI). Differentiating classes of mental tasks from EEG signals is challenging because EEG signals are nonstationary and nonlinear. Owing to its powerful capacity in solving nonlinearity problems, support vector machine (SVM) method has been widely used as a classification tool. Traditional SVMs, however, assume that each feature of a sample contributes equally to classification accuracy, which is not necessarily true in real applications. In addition, the parameters of SVM and the kernel function also affect classification accuracy. In this study, immune feature weighted SVM (IFWSVM) method was proposed. Immune algorithm (IA) was then introduced in searching for the optimal feature weights and the parameters simultaneously. IFWSVM was used to multiclassify five different mental tasks. Theoretical analysis and experimental results showed that IFWSVM has better performance than traditional SVM.
Keywords :
brain-computer interfaces; electroencephalography; independent component analysis; medical signal processing; neurophysiology; signal classification; support vector machines; EEG signals; brain computer interface; immune algorithm; immune feature weighted support vector machines; independent component analysis; kernel function; mental task classification; nonlinearity problems; powerful capacity; Accuracy; Brain modeling; Classification algorithms; Electroencephalography; Immune system; Kernel; Support vector machines; Feature weight; immune algorithm; mental task; support vector machine;
fLanguage :
English
Journal_Title :
Magnetics, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9464
Type :
jour
DOI :
10.1109/TMAG.2010.2072775
Filename :
5560774
Link To Document :
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