  Electroencephalogram (EEG) is one of the most reliable physiological signal
for emotion detection. Being non-stationary in nature, EEGs are better analysed
by spectro temporal representations. Standard features like Discrete Wavelet
Transformation (DWT) can represent temporal changes in spectral dynamics of an
EEG, but is insufficient to extract information other way around, i.e. spectral
changes in temporal dynamics. On the other hand, Empirical mode decomposition
(EMD) based features can be useful to bridge the above mentioned gap. Towards
this direction, we extract two novel features on top of EMD, namely, (a)
marginal hilbert spectrum (MHS) and (b) Holo-Hilbert spectral analysis (HHSA)
based on EMD, to better represent emotions in 2D arousal-valence (A-V) space.
The usefulness of these features for EEG emotion classification is investigated
through extensive experiments using state-of-the-art classifiers. In addition,
experiments conducted on DEAP dataset for binary emotion classification in both
A-V space, reveal the efficacy of the proposed features over the standard set
of temporal and spectral features.
