Classification of Mental Task From EEG Signals Using Immune Feature Weighted Support Vector Machines

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.