用图卷积变分自编码器从脑电数据中学习个体特征，提升识别准确率。
Subject Representation Learning from EEG using Graph Convolutional Variational Autoencoders
- 基于图卷积的变分自编码架构，通过对比学习提取个体特异性表征。
- 在ERP-Core数据集上达89.81%准确率，适配后提升至90.31%。
- 引入注意力适配器，低计算成本实现新个体快速适应，适合跨被试建模。
我们提出GC-VASE，一种基于图卷积的变分自编码器，结合对比学习，用于从脑电数据中进行个体表征学习。该方法利用为个体识别设计的分裂潜在空间架构，成功学习出鲁棒的个体特定潜在表示。为提升模型对未见个体的适应能力，避免大量重训练，我们引入基于注意力的适配网络进行微调，显著降低适应新个体的计算开销。实验表明，该方法显著优于其他深度学习方法，在ERP-Core数据集上达到89.81%的个体平衡准确率，在SleepEDFx-20数据集上达到70.85%。通过适配器与注意力层进行个体自适应微调后，其在ERP-Core上的准确率进一步提升至90.31%。此外，我们进行了详细的消融研究，验证了方法各关键组件的有效性。
原文摘要 · Abstract (English)
We propose GC-VASE, a graph convolutional-based variational autoencoder that leverages contrastive learning for subject representation learning from EEG data. Our method successfully learns robust subject-specific latent representations using the split-latent space architecture tailored for subject identification. To enhance the model's adaptability to unseen subjects without extensive retraining, we introduce an attention-based adapter network for fine-tuning, which reduces the computational cost of adapting the model to new subjects. Our method significantly outperforms other deep learning approaches, achieving state-of-the-art results with a subject balanced accuracy of 89.81% on the ERP-Core dataset and 70.85% on the SleepEDFx-20 dataset. After subject adaptive fine-tuning using adapters and attention layers, GC-VASE further improves the subject balanced accuracy to 90.31% on ERP-Core. Additionally, we perform a detailed ablation study to highlight the impact of the key components of our method.
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