Fine-tuning of pre-processing filters enables scalp-EEG based training of subcutaneous EEG models

The increasing availability of minimally invasive electroencephalogram (EEG) devices for ultra-long-term recordings has opened new possibilities for advanced EEG analysis, but the resulting large amount of generated data leads to a strong need for computational analyses. Deep neural networks (DNNs) have shown to be powerful for this purpose, but the lack of annotated data from these novel devices is a barrier to DNN training. We propose a novel technique based on fine-tuning of linear pre-processing filters, which is capable of compensating for variations in electrode positions and amplifier characteristics and enables training of models for subcutaneous EEG on largely available scalp EEG data. The effectiveness of the method is demonstrated on a state-of-the-art EEG-based sleep scoring model, where we show that the performance on a database used for training can be retained on the subcutaneous EEG by fine-tuning on data from only three subjects.