braindecode.preprocessing.preprocess

braindecode.preprocessing.preprocess(concat_ds, preprocessors, save_dir=None, overwrite=False, n_jobs=None)

Apply preprocessors to a concat dataset.

Parameters

concat_ds: BaseConcatDataset

A concat of BaseDataset or WindowsDataset datasets to be preprocessed.

preprocessors: list(Preprocessor)

List of Preprocessor objects to apply to the dataset.

save_dirstr | None

If a string, the preprocessed data will be saved under the specified directory and the datasets in concat_ds will be reloaded with preload=False.

overwritebool

When save_dir is provided, controls whether to delete the old subdirectories that will be written to under save_dir. If False and the corresponding subdirectories already exist, a FileExistsError will be raised.

n_jobsint | None

Number of jobs for parallel execution.

Returns

BaseConcatDataset:

Preprocessed dataset.

Examples using braindecode.preprocessing.preprocess

Load and save dataset example

Benchmarking preprocessing with parallelization and serialization

MOABB Dataset Example

Process a big data EEG resource (TUH EEG Corpus)

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

Data Augmentation on BCIC IV 2a Dataset

Sleep staging on the Sleep Physionet dataset using Eldele2021

Sleep staging on the Sleep Physionet dataset using Chambon2018 network

Trialwise Decoding on BCIC IV 2a Dataset

Cropped Decoding on BCIC IV 2a Dataset

Fingers flexion decoding on BCIC IV 4 ECoG Dataset

Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset

Self-supervised learning on EEG with relative positioning