Feature selection by independent component analysis and mutual infornation maximization in EEG signal classification
Tian Lan, Deniz Erdoğmuş, André Adami, Misha Pavel · Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. · 2006
Feature selection and dimensionality reduction are important steps in pattern recognition. In this paper, we propose a scheme for feature selection using linear independent component analysis and mutual information maximization method. The method is theoretically motivated by the fact that the classification error rate is related to the mutual information between the feature vectors and the class labels. The feasibility of the principle is illustrated on a synthetic dataset and its performance is demonstrated using EEG signal classification. Experimental results show that this method works well for feature selection.