犬类EEG帮助人类：通过多空间对齐实现跨物种和跨模态的癫痫发作检测
Canine EEG Helps Human: Cross-Species and Cross-Modality Epileptic Seizure Detection via Multi-Space Alignment
AI总结：
本文提出基于跨物种和跨模态EEG数据的多空间对齐方法，利用深度学习技术提升癫痫发作检测能力，实验显示在有限标注数据下，检测准确率超过90%。
摘要：
癫痫对全球健康造成重大影响，影响约6500万人，以及各种动物物种。癫痫的诊断过程常受发作短暂且不可预测的限制。本文提出一种基于跨物种和跨模态EEG数据的多空间对齐方法，通过域适应和知识蒸馏等深度学习技术，对齐跨物种和跨模态的EEG信号，提升检测能力。实验显示，在人类和犬类的多个皮层和脑内EEG数据集上，检测准确率显著提高，实现超过90%的AUC分数，且在目标物种/模态下标注数据极有限的情况下。本文是首个展示整合不同物种和模态异构数据以提升EEG基发作检测性能的研究。该方法可能还可推广至不同脑机接口范式，并表明结合不同物种/模态数据以增加大EEG模型训练数据量的可能。
英文摘要：
Epilepsy significantly impacts global health, affecting about 65 million people worldwide, along with various animal species. The diagnostic processes of epilepsy are often hindered by the transient and unpredictable nature of seizures. Here we propose a multi-space alignment approach based on cross-species and cross-modality electroencephalogram (EEG) data to enhance the detection capabilities and understanding of epileptic seizures. By employing deep learning techniques, including domain adaptation and knowledge distillation, our framework aligns cross-species and cross-modality EEG signals to enhance the detection capability beyond traditional within-species and with-modality models. Experiments on multiple surface and intracranial EEG datasets of humans and canines demonstrated substantial improvements in the detection accuracy, achieving over 90% AUC scores for cross-species and cross-modality seizure detection with extremely limited labeled data from the target species/modality. To our knowledge, this is the first study that demonstrates the effectiveness of integrating heterogeneous data from different species and modalities to improve EEG-based seizure detection performance. The approach may also be generalizable to different brain-computer interface paradigms, and suggests the possibility to combine data from different species/modalities to increase the amount of training data for large EEG models.
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