  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.
