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Deep neural networks (DNNs) used for brain-computer-interface (BCI)classification are commonly expected to learn general features when trainedacross a variety of contexts, such that these features could be fine-tuned tospecific contexts. While some success is found in such an approach, we suggestthat this interpretation is limited and an alternative would better leveragethe newly (publicly) available massive EEG datasets. We consider how to adapttechniques and architectures used for language modelling (LM), that appearcapable of ingesting awesome amounts of data, towards the development ofencephalography modelling (EM) with DNNs in the same vein. We specificallyadapt an approach effectively used for automatic speech recognition, whichsimilarly (to LMs) uses a self-supervised training objective to learncompressed representations of raw data signals. After adaptation to EEG, wefind that a single pre-trained model is capable of modelling completely novelraw EEG sequences recorded with differing hardware, and different subjectsperforming different tasks. Furthermore, both the internal representations ofthis model and the entire architecture can be fine-tuned to a variety ofdownstream BCI and EEG classification tasks, outperforming prior work in moretask-specific (sleep stage classification) self-supervision.

BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG data