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 this https URL.

本文提出了一种双模型方法，结合了基于序列的 EEG 幅值表示和基于图像的表示。采用基于显著性分析的图像模型，促进了两个模型部分的联合学习。在四个公开数据集上进行评估，结果表明本方法优于现有方法，在情感估计任务上取得了较高的稳定性。