提出可跨通道数迁移的脑电分类网络，提升癫痫检测模型泛化能力
SCFNet:A Transferable IIIC EEG Classification Network
- 设计单通道特征提取+后端融合结构，不依赖具体电极数量
- 在IIIC-Seizure数据集上准确率提升4%，优于基线模型
- 仅微调分类头即可保持高性能，适合多设备部署场景
癫痫及癫痫样放电是常见的有害脑活动，脑电图（EEG）信号广泛用于监测患者发作状态。然而，由于缺乏统一的EEG信号采集标准，实际应用中存在诸多障碍，尤其是不同通道数数据间模型迁移困难。为此，本文提出一种基于单通道特征提取（Single Channel Feature）与模型后端融合（SCFNet）的神经网络架构。该模型的特征提取器采用单通道输入的RCNN网络，不依赖其他通道信息，从而更易迁移到不同通道数的数据上。实验表明，在IIIC-Seizure数据集上，EEG-SCFNet的准确率相比基线模型提升4%，较原始RCNN模型提高1.3%。即使仅微调分类头，性能仍可保持与基线相当。此外，在跨数据集迁移中，当电极通道配置不同时，EEG-SCFNet仍能维持一定性能。
原文摘要 · Abstract (English)
Epilepsy and epileptiform discharges are common harmful brain activities, and electroencephalogram (EEG) signals are widely used to monitor the onset status of patients. However, due to the lack of unified EEG signal acquisition standards, there are many obstacles in practical applications, especially the difficulty in transferring and using models trained on different numbers of channels. To address this issue, we proposes a neural network architecture with a single-channel feature extraction (Singal Channel Feature) model backend fusion (SCFNet). The feature extractor of the model is an RCNN network with single-channel input, which does not depend on other channels, thereby enabling easier migration to data with different numbers of channels. Experimental results show that on the IIIC-Seizure dataset, the accuracy of EEG-SCFNet has improved by 4% compared to the baseline model and also increased by 1.3% compared to the original RCNN neural network model. Even with only fine-tuning the classification head, its performance can still maintain a level comparable to the baseline. In addition, in terms of cross-dataset transfer, EEG-SCFNet can still maintain certain performance even if the channel leads are different.
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