Emotion recognition using electroencephalogram (EEG) signals is getting more and more attention in recent years. Since the EEG signals are noisy, non-linear and have non-stationary properties, it is a challenging task to develop an intelligent framework that can provide high accuracy for emotion recognition. In this paper, we propose a new model for emotion recognition that will be based on the creation of feature maps based on the topographic (TOPO-FM) and holographic (HOLO-FM) representation of EEG signal characteristics. Deep learning has been utilized as a feature extractor method on feature maps, and afterward extracted features are fused together for the classification process to recognize different kinds of emotions. The experiments are conducted on the four publicly available emotion datasets: DEAP, SEED, DREAMER, and AMIGOS. We demonstrated the effectiveness of our approaches in comparison with studies where authors used EEG signals that classify human emotions in the two-dimensional space. Experimental results show that the proposed methods can improve the emotion recognition rate on the different size datasets.

近年来，使用脑电图（EEG）信号进行情感识别越来越受到关注。由于脑电信号具有噪声、非线性和非平稳特性，因此开发能够为情绪识别提供高精度的智能框架是一项具有挑战性的任务。在本文中，我们提出了一种新的情感识别模型，该模型将基于基于 EEG 信号特征的地形 (TOPO-FM) 和全息 (HOLO-FM) 表示的特征图的创建。深度学习已被用作特征图上的特征提取方法，然后将提取的特征融合在一起用于分类过程以识别不同种类的情绪。实验是在四个公开可用的情感数据集上进行的：DEAP、SEED、DREAMER 和 AMIGOS。与作者使用 EEG 信号在二维空间中对人类情绪进行分类的研究相比，我们证明了我们的方法的有效性。实验结果表明，所提出的方法可以提高对不同大小数据集的情感识别率。