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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Frontiers in human neuroscience Jg. 15; S. 653659
Hauptverfasser: Kostas, Demetres, Aroca-Ouellette, Stéphane, Rudzicz, Frank
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Frontiers Media S.A 23.06.2021
Schlagworte:
brain computer interface
contrastive learning
convolutional neural network
deep learning - artificial neural network
Human Neuroscience
semi-supervised learning
transformers
ISSN:1662-5161, 1662-5161
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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 that this interpretation is limited and an alternative would better leverage the newly (publicly) available massive electroencephalography (EEG) datasets. We consider how to adapt techniques and architectures used for language modeling (LM) that appear capable of ingesting awesome amounts of data toward the development of encephalography modeling with DNNs in the same vein. We specifically adapt an approach effectively used for automatic speech recognition, which similarly (to LMs) uses a self-supervised training objective to learn compressed representations of raw data signals. After adaptation to EEG, we find that a single pre-trained model is capable of modeling completely novel raw EEG sequences recorded with differing hardware, and different subjects performing different tasks. Furthermore, both the internal representations of this model and the entire architecture can be fine-tuned to a variety of downstream BCI and EEG classification tasks, outperforming prior work in more task-specific (sleep stage classification) self-supervision.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
This article was submitted to Brain-Computer Interfaces, a section of the journal Frontiers in Human Neuroscience
Edited by: Sung Chan Jun, Gwangju Institute of Science and Technology, South Korea
Reviewed by: Dalin Zhang, Aalborg University, Denmark; Tomasz Maciej Rutkowski, RIKEN Center for Advanced Intelligence Project (AIP), Japan
ISSN:1662-5161
1662-5161
DOI:10.3389/fnhum.2021.653659