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...
Uloženo v:
| Vydáno v: | Frontiers in human neuroscience Ročník 15; s. 653659 |
|---|---|
| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Frontiers Media S.A
23.06.2021
|
| Témata: | |
| ISSN: | 1662-5161, 1662-5161 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Shrnutí: | 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. |
|---|---|
| Bibliografie: | 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 |