MAEEG: Masked Auto-encoder for EEG Representation Learning

The temple university hospital eeg data corpus

Iyad Obeid and Joseph Picone · 2016

Earlier work this paper cites.

You snooze, you win: the physionet/computing in cardiology challenge 2018

Mohammad M Ghassemi, Benjamin E Moody, Li-Wei H Lehman, Christopher Song, Qiao Li, Haoqi Sun, Roger G Mark, M Brandon Westover, and Gari D Clifford · 2018

Earlier work this paper cites.

Eegnet: a compact convolutional neural network for eeg-based brain–computer interfaces

Vernon J Lawhern, Amelia J Solon, Nicholas R Waytowich, Stephen M Gordon, Chou P Hung, and Brent J Lance · 2018

Earlier work this paper cites.

wav2vec 2.0: A framework for self-supervised learning of speech representations

Alexei Baevski, Yuhao Zhou, Abdelrahman Mohamed, and Michael Auli · 2020

Earlier work this paper cites.

Contrastive representation learning for electroencephalogram classification

Mostafa Neo Mohsenvand, Mohammad Rasool Izadi, and Pattie Maes · 2020

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Uncovering the structure of clinical eeg signals with self-supervised learning

Hubert Banville, Omar Chehab, Aapo Hyvärinen, Denis-Alexander Engemann, and Alexandre Gramfort · 2021

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Bendr: using transformers and a contrastive self-supervised learning task to learn from massive amounts of eeg data

Demetres Kostas, Stephane Aroca-Ouellette, and Frank Rudzicz · 2021

Cited alongside, same era.

Masked autoencoders are scalable vision learners

Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2022

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Speech emotion: Investigating model representations, multi-task learning and knowledge distillation

Original

Vikramjit Mitra, Hsiang-Yun Sherry Chien, Vasudha Kowtha, Joseph Yitan Cheng, and Erdrin Azemi · 2022

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Masked autoencoders that listen

Original

Hu Xu, Juncheng Li, Alexei Baevski, Michael Auli, Wojciech Galuba, Florian Metze, Christoph Feichtenhofer, et al · 2022

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