README.md

metadata

tags:
  - braindecode
  - eeg
  - neuroscience
  - brain-computer-interface
  - bids
license: unknown

EEG Dataset

This dataset was created using braindecode, a library for deep learning with EEG/MEG/ECoG signals.

Dataset Information

Property	Value
Number of recordings	1
Dataset type	Windowed (from Epochs object)
Number of channels	26
Sampling frequency	250 Hz
Number of windows / samples	48
Total size	0.03 MB
Storage format	zarr
BIDS compatible	Yes

Usage

To load this dataset::

.. code-block:: python

    from braindecode.datasets import BaseConcatDataset

    # Load dataset from Hugging Face Hub
    dataset = BaseConcatDataset.pull_from_hub("username/dataset-name")

    # Access data
    X, y, metainfo = dataset[0]
    # X: EEG data (n_channels, n_times)
    # y: label/target
    # metainfo: window indices

Using with PyTorch DataLoader

::

from torch.utils.data import DataLoader

# Create DataLoader for training
train_loader = DataLoader(
    dataset,
    batch_size=32,
    shuffle=True,
    num_workers=4
)

# Training loop
for X, y, metainfo in train_loader:
    # X shape: [batch_size, n_channels, n_times]
    # y shape: [batch_size]
    # metainfo shape: [batch_size, 2] (start and end indices)
    # Process your batch...

BIDS-like Structure

This dataset follows a BIDS derivatives-like structure for compatibility with neuroimaging tools while maintaining efficiency for deep learning:

derivatives/braindecode/
├── dataset_description.json    # BIDS dataset description
├── participants.tsv            # Subject-level metadata
├── dataset.zarr/               # Main data (optimized for training)
└── sub-<label>/
    └── eeg/
        ├── *_events.tsv        # Trial/window events
        ├── *_channels.tsv      # Channel information
        └── *_eeg.json          # Recording metadata

Accessing BIDS Metadata

After loading the dataset, BIDS metadata is available:

# Access participants info
if hasattr(dataset, "participants"):
    print(dataset.participants)

# Access events for a recording
if hasattr(dataset.datasets[0], "bids_events"):
    print(dataset.datasets[0].bids_events)

# Access channel info
if hasattr(dataset.datasets[0], "bids_channels"):
    print(dataset.datasets[0].bids_channels)

Dataset Format

This dataset is stored in Zarr format, optimized for:

• Fast random access during training (critical for PyTorch DataLoader)
• Efficient compression with blosc
• Cloud-native storage compatibility

For more information about braindecode, visit: https://braindecode.org