Dimensionality reduction of EEG signal using Fuzzy Discernibility Matrix

High dimensionality of feature space is a problem in supervised machine learning. Redundant or superfluous features either slow down the training process or dilute the quality of classification. Many methods are available in literature for dimensionality reduction. Earlier studies explored a discernibility matrix (DM) based reduct calculation for dimensionality reduction. Discernibility matrix works only on discrete values. But most real-world datasets are continuous in nature. Use of traditional discernibility matrix approach inevitably incurs information loss due to discretization. In this paper, we propose a fuzzified adaptation of discernibility matrix with four variants of dissimilarity measure to deal with continuous data. The proposed algorithm has been applied on EEG dataset-III from BCI competition-II. The reduced dataset is then classified using Support Vector Machine (SVM). The performance of the proposed Fuzzy Discernibility Matrix (FDM) variants are compared with original discernibility matrix based method and Principal Component Analysis (PCA). In our empirical study, the proposed method outperforms the other two methods, thus suggesting that it is competitive with them.