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Among the different modalities to assess emotion, electroencephalogram (EEG),representing the electrical brain activity, achieved motivating results overthe last decade. Emotion estimation from EEG could help in the diagnosis orrehabilitation of certain diseases. In this paper, we propose a dual modelconsidering two different representations of EEG feature maps: 1) a sequentialbased representation of EEG band power, 2) an image-based representation of thefeature vectors. We also propose an innovative method to combine theinformation based on a saliency analysis of the image-based model to promotejoint learning of both model parts. The model has been evaluated on fourpublicly available datasets: SEED-IV, SEED, DEAP and MPED. The achieved resultsoutperform results from state-of-the-art approaches for three of the proposeddatasets with a lower standard deviation that reflects higher stability. Forsake of reproducibility, the codes and models proposed in this paper areavailable at https://github.com/VDelv/Emotion-EEG.

A Saliency based Feature Fusion Model for EEG Emotion Estimation