ENH Create transfer learning tutorial#580
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Co-authored-by: Bru <[email protected]>
Co-authored-by: Bru <[email protected]>
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We also have added the code to download and prepare both datasets (TUAB and NMT) and then training and fine-tuning steps. the code works with the newest TUAB dataset and Braindecode. Please let me know if you have any questions. APD_EEG repo. |
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Yes, @MohammadJavadD, you read my mind. this is my block with this PR and the NMT dataset PR. I am thinking of something.... |
bruAristimunha
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Hi @MohammadJavadD,
I made a rapid initial review. We need a little more text/concepts and some figures. Maybe put a static figure from your paper or my paper.
Can you work a little on the text that comments?
| This tutorial shows how-to perform transfer learning using braindecode. | ||
| Indeed, it is known that the best augmentation to use often dependent on the task | ||
| or phenomenon studied. Here we follow the methodology proposed in [1]_ on the | ||
| openly available BCI IV 2a Dataset and another dataset. |
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Can you replace this explanation @MohammadJavadD Something like theses examples:
- https://braindecode.org/stable/auto_examples/model_building/plot_how_train_test_and_tune.html#sphx-glr-auto-examples-model-building-plot-how-train-test-and-tune-py
-https://braindecode.org/dev/auto_examples/model_building/plot_hyperparameter_tuning_with_scikit-learn.html - https://braindecode.org/dev/auto_examples/advanced_training/plot_data_augmentation_search.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-search-py
| REPLACE... Data augmentation could be a step in training deep learning models. | ||
| For decoding brain signals, recent studies have shown that artificially | ||
| generating samples may increase the final performance of a deep learning model [1]_. | ||
| Other studies have shown that data augmentation can be used to cast | ||
| a self-supervised paradigm, presenting a more diverse | ||
| view of the data, both with pretext tasks and contrastive learning [2]_. | ||
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| REPLACE... | ||
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| Figure about transfer learning. |
| # | ||
| # ### Load and save the raw recordings | ||
| # | ||
| # Here we assume you already load and preprocess the raw recordings for both TUAB and NMT datasetsand saved the file in `TUAB_path' and 'NMT_path' respectively. To read more see this notebook [here](https://braindecode.org/stable/auto_examples/applied_examples/plot_tuh_eeg_corpus.html) |
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| # Here we assume you already load and preprocess the raw recordings for both TUAB and NMT datasetsand saved the file in `TUAB_path' and 'NMT_path' respectively. To read more see this notebook [here](https://braindecode.org/stable/auto_examples/applied_examples/plot_tuh_eeg_corpus.html) | |
| # Here, we assume you have already loaded and preprocessed the raw recordings for both TUAB and NMT datasets and saved the file in `TUAB_path` and `NMT_path` respectively. To read more, see how to load the Temple University Dataset in this braindecode [tutorial](https://braindecode.org/stable/auto_examples/applied_examples/plot_tuh_eeg_corpus.html) |
| ###################################################################### | ||
| # Target is being set to pathological | ||
| # ------------------------------------- |
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The concept of target needs to be clear at the begin of the tutorial.
| # We split the recordings by subject into train, validation and | ||
| # testing sets. | ||
| # | ||
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| # split based on train split from dataset |
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Explain a little more about the true and false here, related with abnormal classification
| # ------------------------------------- | ||
| # We can now create the deep learning model. | ||
| # In this tutorial, we use DeepNet introduced in [4]. |
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Can you discuss a little more about the choice of the parameters?
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You can directly use your paper, and say something, as investigated in [1] ...
| print( | ||
| "Number of parameters = ", | ||
| sum(p.numel() for p in model.parameters() if p.requires_grad), | ||
| ) |
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| print( | |
| "Number of parameters = ", | |
| sum(p.numel() for p in model.parameters() if p.requires_grad), | |
| ) | |
| print(clf) |
This is will print the parameters because of EEGClassifier
| print( | ||
| "Number of parameters = ", | ||
| sum(p.numel() for p in model.parameters() if p.requires_grad), | ||
| ) |
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| print( | |
| "Number of parameters = ", | |
| sum(p.numel() for p in model.parameters() if p.requires_grad), | |
| ) | |
| print(clf) |
| ###################################################################### | ||
| # Conclusion | ||
| # ------------------------------------- | ||
| # | ||
| # In this example, we used transfer learning (TL) as a way to learn | ||
| # representations from a large EEG data and transfer to a smaller dataset. | ||
| # You can put one of the results from your paper here. | ||
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You can put more information about why this is important and things like this
bruAristimunha
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