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New Deep Learning Discovery Paves Way for AI Interpretation of Brainwave Data

Interpreting the results of electroencephalogram (EEG) graphs, which are used to visualize brain activity of everything from meditation to neurological disorders, is one of the greatest challenges facing brain researchers. Machine learning has the potential to relieve some of this burden, but EEG data is extremely multidimensional and can be expensive, and time-consuming to annotate. It also requires the deep expertise of neurologists and sleep experts. This means there are typically not enough labelled examples for supervised deep neural networks to learn from in order to create an efficient AI.

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While labelled-EEGs identifying sleep stages and brain activity are scarce, there is ample unlabeled data that exists. In his new paper, Uncovering the Structure of Clinical EEG Signals with Self-supervised Learning, Interaxon Inc researcher Hubert Banville and researchers at Université Paris-Saclay, University of Helsinki, and Max Planck Institute applied self-supervised learning to extract features from unlabeled EEGs. Banville found that when limited numbers of labelled data were available, his self-supervised learning approach outperformed traditional supervised learning methods that rely purely on labelled data. These results were obtained on two very different EEG classification problems: identifying sleep stages in overnight recordings, and detecting EEG pathologies. Moreover, without access to any labelled data, his approach also uncovered a fascinating structure in the data that relates to clinical information such as sleep stages, pathology and age.

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Put into practice, these intelligent new approaches will allow the scientific community, including researchers at Interaxon Inc., the developer of Muse headbands that has one of the largest brain data (EEG) collections in the world, to leverage, mine and utilize large amounts of unlabeled EEG data to efficiently discover relevant information in EEG. This has potential to improve the performance of algorithms used in everything from consumer sleep and wellness support tools like Muse S, to swifter diagnosis of neurological disorders.

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