Apply Self-supervised Learning on NEMAR data
https://www.youtube.com/watch?v=gm3a7T2bmnc
Y. Benchetrit, H. Banville, and J.-R. King, “Brain decoding: toward real-time reconstruction of visual perception,” Mar. 14, 2024, arXiv: arXiv:2310.19812. doi: 10.48550/arXiv.2310.19812.
A. Thual et al., “Aligning brain functions boosts the decoding of visual semantics in novel subjects,” Dec. 11, 2023, arXiv: arXiv:2312.06467. doi: 10.48550/arXiv.2312.06467.
Banville, Hubert & Chehab, Omar & Hyvarinen, Aapo & Engemann, Denis-Alexander & Gramfort, Alexandre. (2020). Uncovering the structure of clinical EEG signals with self-supervised learning. Journal of Neural Engineering. 18. 10.1088/1741-2552/abca18. https://www.researchgate.net/publication/346857471_Uncovering_the_structure_of_clinical_EEG_signals_with_self-supervised_learning
Hubert Banville, Isabela Albuquerque, Aapo Hyvärinen, Graeme Moffat, Denis-Alexander Engemann, et al.. Self-supervised representation learning from electroencephalography signals. MLSP 2019 - IEEE 29th International Workshop on Machine Learning for Signal Processing, Oct 2019, Pittsburgh, United States. ⟨hal-02361350⟩ https://hal.science/hal-02361350
On a machine with Nvidia GPU:
docker run -it --runtime=nvidia --gpus all -v /mnt/nemar/child-mind-rest:/mnt/nemar/child-mind-rest -v .:/app dtyoung/eeg-ssl python main.py --nsubjects=30
Here the path to the dataset is at /mnt/nemar/child-mind-rest and we assume that the command is run in the top-level directory of the cloned version of this repo.