Spectro Temporal EEG Biomarkers For Binary Emotion Classification

TL;DR

This paper introduces novel spectro-temporal EEG features based on Empirical Mode Decomposition, improving binary emotion classification accuracy in arousal-valence space over standard features.

Contribution

It proposes two new EMD-based features, MHS and HHSA, for enhanced EEG emotion classification in 2D A-V space, demonstrating their effectiveness through extensive experiments.

Findings

Proposed features outperform standard spectral and temporal features.

Features improve binary emotion classification accuracy.

Effective in arousal-valence space on DEAP dataset.

Abstract

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.