DistilCLIP-EEG：通过多模态学习和知识蒸馏增强癫痫发作检测
DistilCLIP-EEG: Enhancing Epileptic Seizure Detection Through Multi-modal Learning and Knowledge Distillation
AI总结：
针对现有癫痫检测仅用单模态EEG的问题，提出基于CLIP的多模态模型DistilCLIP-EEG，融合EEG与文本，并用知识蒸馏压缩模型，在三个数据集上准确率超97%，学生模型参数仅为教师的58.1%。
摘要：
癫痫是一种常见的神经系统疾病，其特征是由异常放电引起的突然、短暂的神经元过度活跃发作，可能导致一些精神障碍。现有的大多数用于癫痫检测的深度学习方法仅依赖单模态的脑电图（EEG）信号，忽视了多模态信息的潜在益处。为了解决这一问题，我们提出了一种基于CLIP框架的新型多模态模型DistilCLIP-EEG，该模型整合了EEG信号和文本描述，以捕获癫痫发作的全面特征。该模型包含一个基于Conformer架构的EEG编码器作为文本编码器，以及所提出的可学习BERT（BERT-LP）作为编码器内的提示学习。两者在共享的潜在空间中运行，以实现有效的跨模态表示学习。为了提高效率和适应性，我们引入了一种知识蒸馏方法，其中训练好的DistilCLIP-EEG作为教师模型，指导一个更紧凑的学生模型，以减少训练复杂性和时间。在TUSZ、AUBMC和CHB-MIT数据集上，教师模型和学生模型的准确率均超过97%。在所有数据集上，F1分数始终高于0.94，证明了所提出框架的鲁棒性和可靠性。此外，学生模型的参数数量和模型大小约为教师模型的58.1%，在保持高性能的同时显著降低了模型复杂性和存储需求。这些结果突显了我们提出的模型在基于EEG的癫痫检测中的潜力，并为在资源受限环境中部署轻量级模型奠定了坚实基础。
英文摘要：
Epilepsy is a prevalent neurological disorder marked by sudden, brief episodes of excessive neuronal activity caused by abnormal electrical discharges, which may lead to some mental disorders. Most existing deep learning methods for epilepsy detection rely solely on unimodal EEG signals, neglecting the potential benefits of multimodal information. To address this, we propose a novel multimodal model, DistilCLIP-EEG, based on the CLIP framework, which integrates both EEG signals and text descriptions to capture comprehensive features of epileptic seizures. The model involves an EEG encoder based on the Conformer architecture as a text encoder, the proposed Learnable BERT (BERT-LP) as prompt learning within the encoders. Both operate in a shared latent space for effective cross-modal representation learning. To enhance efficiency and adaptability, we introduce a knowledge distillation method where the trained DistilCLIP-EEG serves as a teacher to guide a more compact student model to reduce training complexity and time. On the TUSZ, AUBMC, and CHB-MIT datasets, both the teacher and student models achieved accuracy rates exceeding 97%. Across all datasets, the F1-scores were consistently above 0.94, demonstrating the robustness and reliability of the proposed framework. Moreover, the student model's parameter count and model size are approximately 58.1% of those of the teacher model, significantly reducing model complexity and storage requirements while maintaining high performance. These results highlight the potential of our proposed model for EEG-based epilepsy detection and establish a solid foundation for deploying lightweight models in resource-constrained settings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
