Implementations of various published works on self-supervised learning approaches to biosignal feature extraction.

• https://github.com/jstranne/mouse_self_supervision
• Banville et al's arxiv.org/pdf/2007.16104.pdf for RP, TS, and CPC upstream SSL tasks
• Cheng et al's https://arxiv.org/pdf/2007.04871.pdf for SACL upstream SSL pipeline
• Mohsenvad et al's http://proceedings.mlr.press/v136/mohsenvand20a/mohsenvand20a.pdf for SeqCLR upstream SSL pipeline
• The ThinkerInvariance (TIDNet) SSL pipeline described in https://iopscience.iop.org/article/10.1088/1741-2552/abb7a7/pdf by Demetres Kostas and Frank Rudzicz, and their associated code at https://github.com/SPOClab-ca/ThinkerInvariance
• PhaseSwap as described in https://arxiv.org/pdf/2009.07664.pdf by Abdelhak Lemkhenter and Paolo Favaro

• Pytorch (and dependencies)
• CUDA
• Currently only tested on Windows 10 OS with NVIDIA GPU and Pytorch (therefore any models/pipelines reliant on LSTMs/GRUs have not been fully tested - see pytorch/pytorch#27837)

• README.md
• data_utils.py
• models.py
• data_loaders.py
• train.py

• list_training_files
• extract_data
• create_windowed_dataset

• Stager_net_practice
• Embedders for RP, TS, CPC
• Downstream classifier model
• PhaseSwap FCN Embedder and Upstream Decoder
• SeqCLR Embedders (Convolutional and Recurrent) as well as Upstream Decoder (though currently the PhaseSwap FCN Embedder and Upstream Decoder - slightly modified - architectures are being used due to limited computational resources)

• data loading function for training RP, TS, CPC, PhaseSwap, and SeqCLR tasks
• data loading function for downstream task

• upstream training functions
• downstream training (with cross-validation)

(last updated 02/06/2021)

• Define/Implement necessary NN models, data preprocessing/loading, upstream training loops, and downstream training loops for TIDNet