Inter-subject or subject-independent emotion recognition has been a challenging task in affective computing. This work is about an easy-to-implement emotion recognition model that classifies emotions from EEG signals subject independently. It is based on the famous EEGNet architecture, which is used in EEG-related BCIs. We used the Dataset on Emotion using Naturalistic Stimuli (DENS) dataset. The dataset contains the Emotional Events -- the precise information of the emotion timings that participants felt. The model is a combination of regular, depthwise and separable convolution layers of CNN to classify the emotions. The model has the capacity to learn the spatial features of the EEG channels and the temporal features of the EEG signals variability with time. The model is evaluated for the valence space ratings. The model achieved an accuracy of 73.04%.

翻译：跨被试或与个体无关的情感识别一直是情感计算领域的挑战性任务。本研究提出一种易于实现的情感识别模型，该模型能够从脑电图信号中独立于被试个体进行情感分类。模型基于著名的EEGNet架构（该架构常用于脑电图相关脑机接口系统），采用自然刺激情感数据集（DENS）进行实验。该数据集包含情绪事件——即参与者产生情感的精确时间信息。模型结合了CNN的常规卷积层、深度可分离卷积层和逐点卷积层以实现情感分类，具备学习脑电通道空间特征以及脑电图信号随时间变化的时序特征的能力。针对效价空间评分的评估结果显示，该模型达到了73.04%的分类准确率。