Classify the number of EEG current sources using support vector machines
Wen-Yn Huang, Xue-Qi Shen, Qing Wu · 2003
The classifier based on support vector machines (SVMs) has had successful applications in many fields for its simple structure and excellent learning performance. In this paper we apply such classifiers to the EEG (electroencephalogram) data and use them to determine the number of EEG current sources according to the scalp potentials. Experimental results indicate that SVM classifiers are an effective and promising approach for this task.