Comparison of IMF Selection Methods in Classification of Multiple Sclerosis EEG Data

Empirical mode decomposition (EMD) method is a powerful tool in the analysis of of nonlinear and nonstationary signals. It decomposes signals into a number of amplitude and frequency modulated signals namely intrinsic mode functions (IMFs). However, some of these IMFs represents the original signal better while some of them are useless. IMF selection methods are suggested to determine the IMFs which represents the original signal better than other IMFs. In this study, we analyzed the effect of IMF selection methods in classification performance. We compared power based, correlation based and power spectral density based IMF selection methods in the classification of the electroencephalography (EEG) signals, which are collected from subjects with multiple sclerosis. The EEG signals are classified as the patients are being cognitively impaired or intact. k-nearest neighbors, multilayer perceptron neural networks and random forest classifiers are used in classification. The results show that, effect of IMF selection methods on accuracy is changeable in regard to classifier preference.