braindecode.samplers.RecordingSampler#

class braindecode.samplers.RecordingSampler(metadata, random_state=None)[source]#

Base sampler simplifying sampling from recordings.

Parameters

  • metadata (pd.DataFrame) –

    DataFrame with at least one of {subject, session, run} columns for each window in the BaseConcatDataset to sample examples from. Normally obtained with BaseConcatDataset.get_metadata(). For instance, metadata.head() might look like this:

    i_window_in_trial i_start_in_trial i_stop_in_trial target subject session run

    0 0 0 500 -1 4 session_T run_0 1 1 500 1000 -1 4 session_T run_0 2 2 1000 1500 -1 4 session_T run_0 3 3 1500 2000 -1 4 session_T run_0 4 4 2000 2500 -1 4 session_T run_0

  • random_state (np.RandomState | int | None) – Random state.

info#

Series with MultiIndex index which contains the subject, session, run and window indices information in an easily accessible structure for quick sampling of windows.

Type

pd.DataFrame

n_recordings#

Number of recordings available.

Type

int

Methods

sample_recording()[source]#

Return a random recording index.

sample_window(rec_ind=None)[source]#

Return a specific window.

Examples using braindecode.samplers.RecordingSampler#

Sleep staging on the Sleep Physionet dataset using U-Sleep network

Sleep staging on the Sleep Physionet dataset using U-Sleep network

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 Eldele2021

Sleep staging on the Sleep Physionet dataset using Eldele2021
Sleep staging on the Sleep Physionet dataset using Chambon2018 network

Sleep staging on the Sleep Physionet dataset using Chambon2018 network

Sleep staging on the Sleep Physionet dataset using Chambon2018 network
Self-supervised learning on EEG with relative positioning

Self-supervised learning on EEG with relative positioning

Self-supervised learning on EEG with relative positioning