April 16, 2024, 4:41 a.m. | Richard Csaky, Mats W. J. van Es, Oiwi Parker Jones, Mark Woolrich

cs.LG updates on arXiv.org arxiv.org

arXiv:2404.09256v1 Announce Type: new
Abstract: Deep learning techniques can be used to first training unsupervised models on large amounts of unlabelled data, before fine-tuning the models on specific tasks. This approach has seen massive success for various kinds of data, e.g. images, language, audio, and holds the promise of improving performance in various downstream tasks (e.g. encoding or decoding brain data). However, there has been limited progress taking this approach for modelling brain signals, such as Magneto-/electroencephalography (M/EEG). Here we …

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