braindecode.preprocessing.preprocess#

braindecode.preprocessing.preprocess(concat_ds, preprocessors, save_dir=None, overwrite=False, n_jobs=None)[source]#

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_dir (str | 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.

  • overwrite (bool) – 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_jobs (int | None) – Number of jobs for parallel execution.

Returns

Preprocessed dataset.

Return type

BaseConcatDataset

Examples using braindecode.preprocessing.preprocess#

Load and save dataset example

Load and save dataset example

Load and save dataset example
Benchmarking preprocessing with parallelization and serialization

Benchmarking preprocessing with parallelization and serialization

Benchmarking preprocessing with parallelization and serialization
MOABB Dataset Example

MOABB Dataset Example

MOABB Dataset Example
Hyperparameter tuning with scikit-learn

Hyperparameter tuning with scikit-learn

Hyperparameter tuning with scikit-learn
Process a big data EEG resource (TUH EEG Corpus)

Process a big data EEG resource (TUH EEG Corpus)

Process a big data EEG resource (TUH EEG Corpus)
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
Data Augmentation on BCIC IV 2a Dataset

Data Augmentation on BCIC IV 2a Dataset

Data Augmentation on BCIC IV 2a Dataset
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
Searching the best data augmentation on BCIC IV 2a Dataset

Searching the best data augmentation on BCIC IV 2a Dataset

Searching the best data augmentation on BCIC IV 2a Dataset
Trialwise Decoding on BCIC IV 2a Dataset

Trialwise Decoding on BCIC IV 2a Dataset

Trialwise Decoding on BCIC IV 2a Dataset
Fingers flexion decoding on BCIC IV 4 ECoG Dataset

Fingers flexion decoding on BCIC IV 4 ECoG Dataset

Fingers flexion decoding on BCIC IV 4 ECoG Dataset
Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset

Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset

Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset
Cropped Decoding on BCIC IV 2a Dataset

Cropped Decoding on BCIC IV 2a Dataset

Cropped Decoding on BCIC IV 2a Dataset
Self-supervised learning on EEG with relative positioning

Self-supervised learning on EEG with relative positioning

Self-supervised learning on EEG with relative positioning
How to train, test and tune your model

How to train, test and tune your model

How to train, test and tune your model