braindecode.datasets.SleepPhysionet#
- class braindecode.datasets.SleepPhysionet(subject_ids=None, recording_ids=None, preload=False, load_eeg_only=True, crop_wake_mins=30, crop=None)[source]#
Sleep Physionet dataset.
Sleep dataset from https://physionet.org/content/sleep-edfx/1.0.0/. Contains overnight recordings from 78 healthy subjects.
See [MNE example](https://mne.tools/stable/auto_tutorials/sample-datasets/plot_sleep.html).
- Parameters
subject_ids (list(int) | int | None) – (list of) int of subject(s) to be loaded. If None, load all available subjects.
recording_ids (list(int) | None) – Recordings to load per subject (each subject except 13 has two recordings). Can be [1], [2] or [1, 2] (same as None).
preload (bool) – If True, preload the data of the Raw objects.
load_eeg_only (bool) – If True, only load the EEG channels and discard the others (EOG, EMG, temperature, respiration) to avoid resampling the other signals.
crop_wake_mins (float) – Number of minutes of wake time to keep before the first sleep event and after the last sleep event. Used to reduce the imbalance in this dataset. Default of 30 mins.
crop (None | tuple) – If not None crop the raw files (e.g. to use only the first 3h). Example:
crop=(0, 3600*3)to keep only the first 3h.
Examples using braindecode.datasets.SleepPhysionet#
Benchmarking preprocessing with parallelization and serialization
Sleep staging on the Sleep Physionet dataset using U-Sleep network
Sleep staging on the Sleep Physionet dataset using Eldele2021
Sleep staging on the Sleep Physionet dataset using Chambon2018 network
Self-supervised learning on EEG with relative positioning