SCFNet:A Transferable IIIC EEG Classification Network
This work addresses a practical obstacle in EEG-based epilepsy monitoring for medical applications, but it is incremental as it builds on existing RCNN methods.
The authors tackled the problem of transferring EEG classification models across datasets with different numbers of channels by proposing SCFNet, a neural network with single-channel feature extraction, which improved accuracy by 4% over the baseline and 1.3% over the original RCNN on the IIIC-Seizure dataset.
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.