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In this study, the Multivariate Empirical Mode Decomposition (MEMD) approachis applied to extract features from multi-channel EEG signals for mental stateclassification. MEMD is a data-adaptive analysis approach which is suitableparticularly for multi-dimensional non-linear signals like EEG. Applying MEMDresults in a set of oscillatory modes called intrinsic mode functions (IMFs).As the decomposition process is data-dependent, the IMFs vary in accordancewith signal variation caused by functional brain activity. Among the extractedIMFs, it is found that those corresponding to high-oscillation modes are mostuseful for detecting different mental states. Non-linear features are computedfrom the IMFs that contribute most to mental state detection. These MEMDfeatures show a significant performance gain over the conventionaltempo-spectral features obtained by Fourier transform and Wavelet transform.The dominance of specific brain region is observed by analysing the MEMDfeatures extracted from associated EEG channels. The frontal region is found tobe most significant with a classification accuracy of 98.06%. Thismulti-dimensional decomposition approach upholds joint channel properties andproduces most discriminative features for EEG based mental state detection.

Multivariate Empirical Mode Decomposition of EEG for Mental State Detection at Localized Brain Lobes