BENDR: Using Transformers and a Contrastive Self-Supervised Learning Task to Learn From Massive Amounts of EEG Data

BENDR: Using Transformers and a Contrastive Self-Supervised Learning Task to Learn From Massive Amounts of EEG Data

Deep neural networks (DNNs) used for brain–computer interface (BCI) classification are commonly expected to learn general features when trained across a variety of contexts, such that these features could be fine-tuned to specific contexts. While some success is found in such an approach, we suggest...

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Bibliographic Details
Main Authors: Demetres Kostas, Stéphane Aroca-Ouellette, Frank Rudzicz
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-06-01
Series:Frontiers in Human Neuroscience
Subjects:
brain computer interface
deep learning - artificial neural network
transformers
semi-supervised learning
contrastive learning
convolutional neural network
Online Access:https://www.frontiersin.org/articles/10.3389/fnhum.2021.653659/full

Internet

https://www.frontiersin.org/articles/10.3389/fnhum.2021.653659/full

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