KempSleepEDF2013#
- class torch_brain.datasets.KempSleepEDF2013(root=None, recording_ids=None, transform=None, uniquify_channel_ids=True, fold_number=0, fold_type='intrasession', dirname='kemp_sleep_edf_2013', **kwargs)[source]#
Bases:
torch_brain.datasets.dataset.DatasetSleep-EDF Database Expanded containing 197 whole-night polysomnographic sleep recordings.
Preprocessing
To download and prepare this dataset, run
brainsets prepare kemp_sleep_edf_2013
- Parameters:
root (
Optional[str]) – Root directory for the dataset. Defaults toprocessed_dirfrom brainsets config.recording_ids (
Optional[list[str]]) – List of recording IDs to load.transform (
Optional[Callable]) – Data transformation to apply.uniquify_channel_ids (
bool) – Whether to prefix channel IDs with session ID to ensure uniqueness. Defaults to True.fold_number (
int) – The cross-validation fold index (0 to 2 for a 3-fold split). Defaults to 0.fold_type (
Literal['intrasession','intersubject','intersession']) – The splitting strategy. Must be one of: - “intrasession”: Epoch-level stratified split within each session. - “intersubject”: Subject-level split (subjects are assigned to train/valid/test). - “intersession”: Session-level split (subject-session pairs are assigned to train/valid/test). Defaults to “intrasession”.dirname (
str) – Subdirectory for the dataset. Defaults to “kemp_sleep_edf_2013”.
- get_sampling_intervals(split=None)[source]#
Returns a dictionary of sampling intervals for each recording. This represents the intervals that can be sampled from each session.
This dictionary will be used by
torch_brain’s Samplers to know where to sample from.The default method returns intervals containing the entire domain of each recording. This behavior can be overridden by subclasses to give out custom sampling intervals.
- Returns:
Dictionary mapping recording IDs to their time domain intervals.