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TF-C Pretrain Epilepsy

posted on 2023-01-17, 02:46 authored by Xiang ZhangXiang Zhang, Ziyuan ZhaoZiyuan Zhao, Theodoros Tsiligkaridis, Marinka Zitnik

- Paper: Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency

- Paper link: 

- Github repo:

- Project website: 

Epilepsy contains single-channel EEG measurements from 500 subjects. For each subject, the brain activity was recorded for 23.6 seconds. The dataset was then divided and shuffled (to mitigate sample-subject association) into 11,500 samples of 1 second each, sampled at 178 Hz. The raw dataset features 5 different classification labels corresponding to different status of the subject or location of measurement - eyes open, eyes closed, EEG measured in healthy brain region, EEG measured where the tumor was located, and, finally, the subject experiencing seizure episode. To emphasize the distinction between positive and negative samples in terms of epilepsy, We merge the first 4 classes into one and each time series sample has a binary label describing if the associated subject is experiencing seizure or not.