TF-C Pretrain Epilepsy

- Paper: Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency - Paper link: - Github repo: https://github.com/mims-harvard/TFC-pretraining - Project website: <br> <strong>Epilepsy</strong> 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.

论文：基于时频一致性的时序自监督对比预训练；论文链接：无；GitHub仓库：https://github.com/mims-harvard/TFC-pretraining；项目网站：<br><strong>癫痫数据集</strong>包含500名受试者的单通道脑电图（Electroencephalogram, EEG）测量数据。每名受试者的脑电活动被记录时长为23.6秒。随后该数据集被拆分并打乱（以消除样本与受试者的关联偏倚），得到11500条时长为1秒的采样样本，采样率为178赫兹。原始数据集包含5种不同的分类标签，分别对应受试者的不同状态或测量位置：睁眼状态、闭眼状态、健康脑区的脑电测量、肿瘤所在位置的脑电测量，以及受试者出现癫痫发作的状态。为了突出癫痫相关正负样本的区分度，我们将前4个类别合并为一类，此时每条时序样本将带有一个二分类标签，用于标记对应受试者是否正在经历癫痫发作。