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

TL;DR

This paper introduces a multivariate empirical mode decomposition method for extracting features from multi-channel EEG signals, significantly improving mental state classification accuracy by focusing on localized brain lobes.

Contribution

It applies MEMD to EEG signals for the first time to enhance mental state detection, emphasizing the importance of high-oscillation modes and localized brain regions.

Findings

High classification accuracy of 98.06% in frontal lobe.

MEMD features outperform Fourier and Wavelet features.

Localized brain regions significantly influence detection performance.

Abstract

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