Emotion classification through EEG signals plays a significant role in psychology, neuroscience, and human-computer interaction. This paper addresses the challenge of mapping human emotions using EEG data in the Mapping Human Emotions through EEG Signals FG24 competition. Subjects mimic the facial expressions of an avatar, displaying fear, joy, anger, sadness, disgust, and surprise in a VR setting. EEG data is captured using a multi-channel sensor system to discern brain activity patterns. We propose a novel two-stream neural network employing a Bi-Hemispheric approach for emotion inference, surpassing baseline methods and enhancing emotion recognition accuracy. Additionally, we conduct a temporal analysis revealing that specific signal intervals at the beginning and end of the emotion stimulus sequence contribute significantly to improve accuracy. Leveraging insights gained from this temporal analysis, our approach offers enhanced performance in capturing subtle variations in the states of emotions.

翻译：通过脑电图（EEG）信号进行情绪分类在心理学、神经科学和人机交互中发挥着重要作用。本文针对FG24“通过脑电图信号映射人类情绪”竞赛中利用EEG数据映射人类情绪的挑战展开研究。受试者在虚拟现实环境中模仿虚拟人的面部表情，表现出恐惧、喜悦、愤怒、悲伤、厌恶和惊讶六种情绪。通过多通道传感器系统捕获EEG数据，以辨识大脑活动模式。我们提出一种新颖的双流神经网络，采用双半球方法进行情绪推理，超越了基线方法，提升了情绪识别准确率。此外，我们开展时间分析，发现情绪刺激序列开始和结束阶段的特定信号区间对提高准确率贡献显著。利用从该时间分析中获得的洞察，我们的方法在捕捉情绪状态的细微变化方面展现出更优性能。