Research on Classification Method of Combining Support Vector Machine and Genetic Algorithm for Motor Imagery EEG
نویسندگان
چکیده
The support vector machine (SVM) shows many unique advantages in solving the small sample, nonlinear and high dimensional pattern recognition problems, and it is very suitable to solve the classification problem in motor imagery EEG. For SVM using radial basis function (RBF) kernel, two parameters had to be selected beforehand: the trade-off parameter C and the kernel parameter σ. The traditional Grid Search method always requires setting the range of appropriate parameters set beforehand, so it is difficult to obtain the best effect. However, genetic algorithm (GA) has good global search capability, and it can efficiently solve the optimization problem. Therefore, this contribution reports on the robust pattern classification for motor imagery EEG using a combined approach of SVM and GA. And by comparing the max classification accuracy obtained by GA and Grid Search, respectively, it verifies the feasibility and efficiency of the method proposed in this paper.
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