Mendivil Sauceda Jesus Arturo, Marquez Bogart Yail, Esqueda Elizondo José Jaime
Tecnológico Nacional de México, Campus Tijuana. Calz del Tecnológico 12950, Tomas Aquino, Tijuana 22414, Mexico.
Facultad de Ciencias Químicas e Ingeniería, Universidad Autónoma de Baja California, Calzada Universidad 14418, Parque Industrial Internacional, Tijuana 22390, Mexico.
Brain Sci. 2024 Nov 29;14(12):1211. doi: 10.3390/brainsci14121211.
Emotions significantly influence decision-making, social interactions, and medical outcomes. Leveraging emotion recognition through Electroencephalography (EEG) signals offers potential advancements in personalized medicine, adaptive technologies, and mental health diagnostics. This study aimed to evaluate the performance of three neural network architectures-ShallowFBCSPNet, Deep4Net, and EEGNetv4-for emotion classification using the SEED-V dataset.
The SEED-V dataset comprises EEG recordings from 16 individuals exposed to 15 emotion-eliciting video clips per session, targeting happiness, sadness, disgust, neutrality, and fear. EEG data were preprocessed with a bandpass filter, segmented by emotional episodes, and split into training (80%) and testing (20%) sets. Three neural networks were trained and evaluated to classify emotions from the EEG signals.
ShallowFBCSPNet achieved the highest accuracy at 39.13%, followed by Deep4Net (38.26%) and EEGNetv4 (25.22%). However, significant misclassification issues were observed, such as EEGNetv4 predicting all instances as "Disgust" or "Neutral" depending on the configuration. Compared to state-of-the-art methods, such as ResNet18 combined with differential entropy, which achieved 95.61% accuracy on the same dataset, the tested models demonstrated substantial limitations.
Our results highlight the challenges of generalizing across emotional states using raw EEG signals, emphasizing the need for advanced preprocessing and feature-extraction techniques. Despite these limitations, this study provides valuable insights into the potential and constraints of neural networks for EEG-based emotion recognition, paving the way for future advancements in the field.
情绪对决策、社交互动和医疗结果有显著影响。通过脑电图（EEG）信号进行情绪识别，在个性化医疗、自适应技术和心理健康诊断方面具有潜在的进展。本研究旨在使用SEED-V数据集评估三种神经网络架构——浅FBCSPNet、Deep4Net和EEGNetv4——在情绪分类方面的性能。
SEED-V数据集包含16名个体的脑电图记录，每个会话中这些个体观看15个引发情绪的视频片段，目标情绪包括快乐、悲伤、厌恶、中性和恐惧。EEG数据经过带通滤波器预处理，按情绪片段进行分割，并分为训练集（80%）和测试集（20%）。训练并评估了三个神经网络，以从EEG信号中分类情绪。
浅FBCSPNet的准确率最高，为39.13%，其次是Deep4Net（38.26%）和EEGNetv4（25.22%）。然而，观察到了显著的误分类问题，例如EEGNetv4根据配置将所有实例预测为“厌恶”或“中性”。与最先进的方法相比，如在同一数据集上准确率达到95.61%的ResNet18与微分熵相结合的方法，测试模型显示出明显的局限性。
我们的结果凸显了使用原始EEG信号对不同情绪状态进行泛化的挑战，强调了先进的预处理和特征提取技术的必要性。尽管存在这些局限性，本研究为基于EEG的情绪识别中神经网络的潜力和限制提供了有价值的见解，为该领域未来的进展铺平了道路。
