Contrastive Learning for Self-supervised Clustering of iEEG Data for Epileptic Patients
Epilepsy is a severe and prevalent chronic neurological disorder affecting 1–2% of the world’s population. One-third of epilepsy patients continue to suffer from seizures despite the best possible pharmacological treatment. For these patients with so-called drug-resistant epilepsy, various algorithms based on intracranial electroencephalography (iEEG) recording are proposed to detect the onset of seizures. Training accurate models (e.g., convolutional neural networks) for the detection of seizure onsets requires a large amount of labeled data. Indeed, labeling can be particularly challenging for certain types of data that are highly complex or noisy, resulting in poor quality human annotations at best.
A promising alternative is to train the models in a self-supervised way using contrastive learning , which was able to learn different sleep states. This will not only improve seizure onset detection accuracy but also gives important insights into the features of the model. You will start with a given model for seizure detection and apply it to contrastive learning. We use the publicly available long-term dataset  consisting of a total of 2656 hours iEEG recordings.
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-  H. Banville et al., Self-supervised representation learning from electroencephalography signals, 2019
-  iEEG-SWEZ data base