A Saliency based Feature Fusion Model for EEG Emotion Estimation
This work addresses emotion estimation for potential medical applications like disease diagnosis or rehabilitation, but it is incremental as it builds on existing EEG feature methods.
The paper tackled emotion estimation from EEG signals by proposing a dual model with sequential and image-based representations, using saliency analysis for fusion, and achieved state-of-the-art results on three out of four datasets with higher stability.
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.