Spectro Temporal EEG Biomarkers For Binary Emotion Classification
This work addresses emotion detection for applications like human-computer interaction, but it is incremental as it builds on existing EMD methods with new features for a specific dataset.
The paper tackled binary emotion classification from EEG signals by proposing novel spectro-temporal features based on Empirical Mode Decomposition (EMD), specifically marginal Hilbert spectrum and Holo-Hilbert spectral analysis, and demonstrated their efficacy over standard features on the DEAP dataset.
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.