新模型能自动识别脑电信号异常点并定位源头，适应不同设备配置。
Nested Deep Learning Model Towards A Foundation Model for Brain Signal Data
- 用加权融合多通道信号，可适配不同设备设置。
- 在真实数据上准确识别癫痫波，并定位起源通道。
- 适合临床医生快速分析脑电图，提升诊断效率。
全球约有5000万癫痫患者。基于脑电图（EEG）或脑磁图（MEG）的棘波检测在诊断与治疗中至关重要。人工识别耗时且需专业训练，限制了合格医生数量。现有算法难以应对不同通道配置，且无法准确定位棘波来源通道。本文提出一种新型嵌套深度学习（Nested Deep Learning, NDL）框架，通过全通道信号的加权组合，实现对不同通道配置的自适应，并帮助临床医生更准确地识别关键通道。理论分析与真实EEG/MEG数据集的实证验证表明，NDL不仅提升预测精度，还能实现通道定位，支持跨模态数据融合，适用于多种神经生理学应用场景。
原文摘要 · Abstract (English)
Epilepsy affects around 50 million people globally. Electroencephalography (EEG) or Magnetoencephalography (MEG) based spike detection plays a crucial role in diagnosis and treatment. Manual spike identification is time-consuming and requires specialized training that further limits the number of qualified professionals. To ease the difficulty, various algorithmic approaches have been developed. However, the existing methods face challenges in handling varying channel configurations and in identifying the specific channels where the spikes originate. A novel Nested Deep Learning (NDL) framework is proposed to overcome these limitations. NDL applies a weighted combination of signals across all channels, ensuring adaptability to different channel setups, and allows clinicians to identify key channels more accurately. Through theoretical analysis and empirical validation on real EEG/MEG datasets, NDL is shown to improve prediction accuracy, achieve channel localization, support cross-modality data integration, and adapt to various neurophysiological applications.
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