braindecode.datasets.MOABBDataset

class braindecode.datasets.MOABBDataset(dataset_name, subject_ids=None, dataset_kwargs=None, dataset_load_kwargs=None)

A class for moabb datasets.

Parameters:

• dataset_name (str) – name of dataset included in moabb to be fetched

• subject_ids (list(int) | int | None) – (list of) int of subject(s) to be fetched. If None, data of all subjects is fetched.

• dataset_kwargs (dict, optional) – optional dictionary containing keyword arguments to pass to the moabb dataset when instantiating it.

• dataset_load_kwargs (dict, optional) – optional dictionary containing keyword arguments to pass to the moabb dataset’s load_data method. Allows using the moabb cache_config=None and process_pipeline=None.

Examples using braindecode.datasets.MOABBDataset

Cleaning EEG Data with EEGPrep for Trialwise Decoding

Cleaning EEG Data with EEGPrep for Trialwise Decoding

Cropped Decoding on BCIC IV 2a Dataset

Cropped Decoding on BCIC IV 2a Dataset

Basic Brain Decoding on EEG Data

Basic Brain Decoding on EEG Data

How to train, test and tune your model?

How to train, test and tune your model?

Hyperparameter tuning with scikit-learn

Hyperparameter tuning with scikit-learn

Comprehensive Preprocessing with MNE-based Classes

Comprehensive Preprocessing with MNE-based Classes

Training a Braindecode model in PyTorch

Training a Braindecode model in PyTorch

Uploading and downloading datasets to Hugging Face Hub

Uploading and downloading datasets to Hugging Face Hub

Load and save dataset example

Load and save dataset example

MOABB Dataset Example

MOABB Dataset Example

Split Dataset Example

Split Dataset Example

Data Augmentation on BCIC IV 2a Dataset

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

Fine-tuning a Foundation Model (Signal-JEPA)

Fine-tuning a Foundation Model (Signal-JEPA)

Fixed-Length Windows Extraction

Fixed-Length Windows Extraction