MEMD-HHT based Emotion Detection from EEG using 3D CNN

TL;DR

This paper introduces a novel EEG-based emotion detection method using MEMD to extract features, then applying a 3D CNN to classify valence and arousal levels with high accuracy, outperforming previous systems.

Contribution

The study combines MEMD-derived IMFs and MHS with 3D CNNs for improved emotion detection from EEG signals, demonstrating superior accuracy over existing methods.

Findings

Achieved 89.25% accuracy for valence classification.

Achieved 86.23% accuracy for arousal classification.

Significantly outperformed previous 2D CNN and feature-based systems.

Abstract

In this study, the Multivariate Empirical Mode Decomposition (MEMD) is applied to multichannel EEG to obtain scale-aligned intrinsic mode functions (IMFs) as input features for emotion detection. The IMFs capture local signal variation related to emotion changes. Among the extracted IMFs, the high oscillatory ones are found to be significant for the intended task. The Marginal Hilbert spectrum (MHS) is computed from the selected IMFs. A 3D convolutional neural network (CNN) is adopted to perform emotion detection with spatial-temporal-spectral feature representations that are constructed by stacking the multi-channel MHS over consecutive signal segments. The proposed approach is evaluated on the publicly available DEAP database. On binary classification of valence and arousal level (high versus low), the attained accuracies are 89.25% and 86.23% respectively, which significantly outperform previously reported systems with 2D CNN and/or conventional temporal and spectral features.