用CNN-LSTM-注意力模型从脑电波识别压力状态，准确率达81.25%。
Brain2Vec: A Deep Learning Framework for EEG-Based Stress Detection Using CNN-LSTM-Attention
- 融合卷积、循环和注意力机制，分步提取脑电信号的空间、时序与关键特征。
- 在DEAP数据集上达到81.25%验证准确率和0.68的AUC值。
- 适合可穿戴压力监测与个性化健康系统开发人员参考。
心理压力已成为影响认知健康与整体福祉的普遍因素，亟需发展鲁棒、非侵入式的诊断工具。脑电图（EEG）信号能直接反映神经活动，但其非平稳性和高维度特性带来了显著建模挑战。本文提出Brain2Vec，一种基于卷积、循环与注意力机制混合架构的深度学习模型，用于从原始EEG记录中分类压力状态。模型首先通过卷积层捕捉局部空间依赖，再经由LSTM层建模序列时间模式，最后利用注意力机制突出信息量大的时间区域。我们在DEAP数据集上进行评估，采用带通滤波、z-score标准化和分段处理作为预处理流程。相比传统CNN-LSTM基线模型，本模型实现0.68的AUC值和81.25%的验证准确率，表明其具备集成于可穿戴压力监测平台与个性化医疗系统的潜力。
原文摘要 · Abstract (English)
Mental stress has become a pervasive factor affecting cognitive health and overall well-being, necessitating the development of robust, non-invasive diagnostic tools. Electroencephalogram (EEG) signals provide a direct window into neural activity, yet their non-stationary and high-dimensional nature poses significant modeling challenges. Here we introduce Brain2Vec, a new deep learning tool that classifies stress states from raw EEG recordings using a hybrid architecture of convolutional, recurrent, and attention mechanisms. The model begins with a series of convolutional layers to capture localized spatial dependencies, followed by an LSTM layer to model sequential temporal patterns, and concludes with an attention mechanism to emphasize informative temporal regions. We evaluate Brain2Vec on the DEAP dataset, applying bandpass filtering, z-score normalization, and epoch segmentation as part of a comprehensive preprocessing pipeline. Compared to traditional CNN-LSTM baselines, our proposed model achieves an AUC score of 0.68 and a validation accuracy of 81.25%. These findings demonstrate Brain2Vec's potential for integration into wearable stress monitoring platforms and personalized healthcare systems.
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