Domain-guided Self-supervision of EEG Data Improves Downstream Classification Performance and Generalizability

Neeraj Wagh, Jionghao Wei, Samarth Rawal, Brent Berry, Leland Barnard, Benjamin Brinkmann, Gregory Worrell, David Jones, Yogatheesan Varatharajah
Proceedings of Machine Learning for Health, PMLR 158:130-142, 2021.

Abstract

This paper presents a domain-guided approach for learning representations of scalp-electroencephalograms (EEGs) without relying on expert annotations. Expert labeling of EEGs has proven to be an unscalable process with low inter-reviewer agreement because of the complex and lengthy nature of EEG recordings. Hence, there is a need for machine learning (ML) approaches that can leverage expert domain knowledge without incurring the cost of labor-intensive annotations. Self-supervised learning (SSL) has shown promise in such settings, although existing SSL efforts on EEG data do not fully exploit EEG domain knowledge. Furthermore, it is unclear to what extent SSL models generalize to unseen tasks and datasets. Here we explore whether SSL tasks derived in a domain-guided fashion can learn generalizable EEG representations. Our contributions are three-fold: 1) we propose novel SSL tasks for EEG based on the spatial similarity of brain activity, underlying behavioral states, and age-related differences; 2) we present evidence that an encoder pretrained using the proposed SSL tasks shows strong predictive performance on multiple downstream classifications; and 3) using two large EEG datasets, we show that our encoder generalizes well to multiple EEG datasets during downstream evaluations.

Cite this Paper

BibTeX
@InProceedings{pmlr-v158-wagh21a, title = {Domain-guided Self-supervision of EEG Data Improves Downstream Classification Performance and Generalizability}, author = {Wagh, Neeraj and Wei, Jionghao and Rawal, Samarth and Berry, Brent and Barnard, Leland and Brinkmann, Benjamin and Worrell, Gregory and Jones, David and Varatharajah, Yogatheesan}, booktitle = {Proceedings of Machine Learning for Health}, pages = {130--142}, year = {2021}, editor = {Roy, Subhrajit and Pfohl, Stephen and Rocheteau, Emma and Tadesse, Girmaw Abebe and Oala, Luis and Falck, Fabian and Zhou, Yuyin and Shen, Liyue and Zamzmi, Ghada and Mugambi, Purity and Zirikly, Ayah and McDermott, Matthew B. A. and Alsentzer, Emily}, volume = {158}, series = {Proceedings of Machine Learning Research}, month = {04 Dec}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v158/wagh21a/wagh21a.pdf}, url = {https://proceedings.mlr.press/v158/wagh21a.html}, abstract = {This paper presents a domain-guided approach for learning representations of scalp-electroencephalograms (EEGs) without relying on expert annotations. Expert labeling of EEGs has proven to be an unscalable process with low inter-reviewer agreement because of the complex and lengthy nature of EEG recordings. Hence, there is a need for machine learning (ML) approaches that can leverage expert domain knowledge without incurring the cost of labor-intensive annotations. Self-supervised learning (SSL) has shown promise in such settings, although existing SSL efforts on EEG data do not fully exploit EEG domain knowledge. Furthermore, it is unclear to what extent SSL models generalize to unseen tasks and datasets. Here we explore whether SSL tasks derived in a domain-guided fashion can learn generalizable EEG representations. Our contributions are three-fold: 1) we propose novel SSL tasks for EEG based on the spatial similarity of brain activity, underlying behavioral states, and age-related differences; 2) we present evidence that an encoder pretrained using the proposed SSL tasks shows strong predictive performance on multiple downstream classifications; and 3) using two large EEG datasets, we show that our encoder generalizes well to multiple EEG datasets during downstream evaluations.} }
Endnote
%0 Conference Paper %T Domain-guided Self-supervision of EEG Data Improves Downstream Classification Performance and Generalizability %A Neeraj Wagh %A Jionghao Wei %A Samarth Rawal %A Brent Berry %A Leland Barnard %A Benjamin Brinkmann %A Gregory Worrell %A David Jones %A Yogatheesan Varatharajah %B Proceedings of Machine Learning for Health %C Proceedings of Machine Learning Research %D 2021 %E Subhrajit Roy %E Stephen Pfohl %E Emma Rocheteau %E Girmaw Abebe Tadesse %E Luis Oala %E Fabian Falck %E Yuyin Zhou %E Liyue Shen %E Ghada Zamzmi %E Purity Mugambi %E Ayah Zirikly %E Matthew B. A. McDermott %E Emily Alsentzer %F pmlr-v158-wagh21a %I PMLR %P 130--142 %U https://proceedings.mlr.press/v158/wagh21a.html %V 158 %X This paper presents a domain-guided approach for learning representations of scalp-electroencephalograms (EEGs) without relying on expert annotations. Expert labeling of EEGs has proven to be an unscalable process with low inter-reviewer agreement because of the complex and lengthy nature of EEG recordings. Hence, there is a need for machine learning (ML) approaches that can leverage expert domain knowledge without incurring the cost of labor-intensive annotations. Self-supervised learning (SSL) has shown promise in such settings, although existing SSL efforts on EEG data do not fully exploit EEG domain knowledge. Furthermore, it is unclear to what extent SSL models generalize to unseen tasks and datasets. Here we explore whether SSL tasks derived in a domain-guided fashion can learn generalizable EEG representations. Our contributions are three-fold: 1) we propose novel SSL tasks for EEG based on the spatial similarity of brain activity, underlying behavioral states, and age-related differences; 2) we present evidence that an encoder pretrained using the proposed SSL tasks shows strong predictive performance on multiple downstream classifications; and 3) using two large EEG datasets, we show that our encoder generalizes well to multiple EEG datasets during downstream evaluations.
APA
Wagh, N., Wei, J., Rawal, S., Berry, B., Barnard, L., Brinkmann, B., Worrell, G., Jones, D. & Varatharajah, Y.. (2021). Domain-guided Self-supervision of EEG Data Improves Downstream Classification Performance and Generalizability. Proceedings of Machine Learning for Health, in Proceedings of Machine Learning Research 158:130-142 Available from https://proceedings.mlr.press/v158/wagh21a.html.

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