A Saliency based Feature Fusion Model for EEG Emotion Estimation

TL;DR

This paper introduces a dual EEG feature fusion model using saliency analysis for improved emotion estimation, demonstrating superior stability and accuracy across multiple datasets.

Contribution

It presents a novel saliency-based fusion approach combining sequential and image-based EEG features for emotion recognition.

Findings

Outperforms state-of-the-art on three datasets

Achieves lower standard deviation indicating higher stability

Provides reproducible code and models

Abstract

Among the different modalities to assess emotion, electroencephalogram (EEG), representing the electrical brain activity, achieved motivating results over the last decade. Emotion estimation from EEG could help in the diagnosis or rehabilitation of certain diseases. In this paper, we propose a dual model considering two different representations of EEG feature maps: 1) a sequential based representation of EEG band power, 2) an image-based representation of the feature vectors. We also propose an innovative method to combine the information based on a saliency analysis of the image-based model to promote joint learning of both model parts. The model has been evaluated on four publicly available datasets: SEED-IV, SEED, DEAP and MPED. The achieved results outperform results from state-of-the-art approaches for three of the proposed datasets with a lower standard deviation that reflects higher stability. For sake of reproducibility, the codes and models proposed in this paper are available at https://github.com/VDelv/Emotion-EEG.