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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| Vydané v: | Frontiers in human neuroscience Ročník 15; s. 653659 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Frontiers Media S.A
23.06.2021
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| ISSN: | 1662-5161, 1662-5161 |
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| Abstract | 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. |
|---|---|
| AbstractList | 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. 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. 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.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. |
| Author | Kostas, Demetres Aroca-Ouellette, Stéphane Rudzicz, Frank |
| AuthorAffiliation | 1 Department Computer Science, University of Toronto , Toronto, ON , Canada 2 Vector Institute for Artificial Intelligence , Toronto, ON , Canada 3 Li Ka Shing Knowledge Institute, St. Michael's Hospital , Toronto, ON , Canada |
| AuthorAffiliation_xml | – name: 3 Li Ka Shing Knowledge Institute, St. Michael's Hospital , Toronto, ON , Canada – name: 1 Department Computer Science, University of Toronto , Toronto, ON , Canada – name: 2 Vector Institute for Artificial Intelligence , Toronto, ON , Canada |
| Author_xml | – sequence: 1 givenname: Demetres surname: Kostas fullname: Kostas, Demetres – sequence: 2 givenname: Stéphane surname: Aroca-Ouellette fullname: Aroca-Ouellette, Stéphane – sequence: 3 givenname: Frank surname: Rudzicz fullname: Rudzicz, Frank |
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| Cites_doi | 10.3390/s20072034 10.1007/s11263-019-01198-w 10.1109/CVPR.2009.5206848 10.1109/TBME.2017.2742541 10.3389/fnhum.2017.00334 10.1007/s10548-009-0121-6 10.3389/fnins.2012.00055 10.1016/j.neuroimage.2006.09.024 10.1073/pnas.1907373117 10.1109/TNNLS.2019.2900046 10.1161/01.cir.101.23.e215 10.1162/tacl_a_00300 10.1371/journal.pone.0216456 10.1109/MLSP.2019.8918693 10.1109/JBHI.2020.2967128 10.1016/j.jneumeth.2015.01.033 10.1016/j.neuroimage.2020.117021 10.1088/1741-2552/abc902 10.1038/nature14539 10.1088/1741-2552/aab2f2 10.1101/2020.12.17.423197 10.1038/s41598-019-38612-9 10.1109/ICASSP40776.2020.9054224 10.3389/fnins.2016.00196 10.1088/1741-2560/7/5/056006 10.1109/ACCESS.2019.2930958 10.1109/CVPR.2016.90 10.1088/1741-2552/ab260c 10.1109/TNSRE.2018.2813138 10.1155/2012/578295 10.1371/journal.pone.0207351 10.1016/j.eswa.2018.08.031 10.1007/978-3-642-24797-2 10.1088/1741-2552/abca18 10.1109/TBME.2004.827072 10.1109/ACCESS.2019.2919143 10.1088/1741-2552/aaf3f6 10.1002/hbm.23730 10.1109/10.867928 10.1038/s41591-018-0171-y 10.1088/1741-2552/aace8c 10.21437/Interspeech.2020-1228 10.1088/1741-2552/abb7a7 |
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| Copyright | Copyright © 2021 Kostas, Aroca-Ouellette and Rudzicz. Copyright © 2021 Kostas, Aroca-Ouellette and Rudzicz. 2021 Kostas, Aroca-Ouellette and Rudzicz |
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