EEG-Based Emotion Recognition Using Hierarchical Network With Subnetwork Nodes

Abstract

Emotions play a crucial role in decision-making, brain activity, human cognition, and social intercourse. This paper proposes a hierarchical network structure with subnetwork nodes to discriminate three human emotions: 1) positive; 2) neutral; and 3) negative. Each subnetwork node embedded in the network that are formed by hundreds of hidden nodes, could be functional as an independent hidden layer for feature representation. The top layer of the hierarchical network, like the mammal cortex in the brain, combine such features generated from subnetwork nodes, but simultaneously, recast these features into a mapping space so that the network can be performed to produce more reliable cognition. The proposed method is compared with other state-of-the-art methods. The experimental results from two different EEG datasets show that a promising result is obtained when using the proposed method with both single and multiple modality.