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In this study, the Multivariate Empirical Mode Decomposition (MEMD) isapplied to multichannel EEG to obtain scale-aligned intrinsic mode functions(IMFs) as input features for emotion detection. The IMFs capture local signalvariation related to emotion changes. Among the extracted IMFs, the highoscillatory ones are found to be significant for the intended task. TheMarginal Hilbert spectrum (MHS) is computed from the selected IMFs. A 3Dconvolutional neural network (CNN) is adopted to perform emotion detection withspatial-temporal-spectral feature representations that are constructed bystacking the multi-channel MHS over consecutive signal segments. The proposedapproach is evaluated on the publicly available DEAP database. On binaryclassification of valence and arousal level (high versus low), the attainedaccuracies are 89.25% and 86.23% respectively, which significantly outperformpreviously reported systems with 2D CNN and/or conventional temporal andspectral features.

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