Note

Go to the end to download the full example code.

Uploading and downloading datasets to Hugging Face Hub#

This example demonstrates how to upload and download EEG datasets to/from the Hugging Face Hub using braindecode.

The Hub integration supports three dataset types:

WindowsDataset - Epoched data (mne.Epochs-based)
EEGWindowsDataset - Continuous raw data with windowing metadata
RawDataset - Continuous raw data without windowing

The Hub integration allows you to:

Share datasets with the research community
Version control your datasets with git-like versioning
Collaborate on dataset curation and preprocessing
Access datasets from anywhere with automatic caching

We’ll use the braindecode.datasets.BNCI2014_001 dataset as an example.

# Authors: Kuntal Kokate
#
# License: BSD (3-clause)

import os

from huggingface_hub import login

from braindecode.datasets import BNCI2014_001, BaseConcatDataset
from braindecode.preprocessing import (
    create_windows_from_events,
)

# Login to Hugging Face Hub using token from environment variable
hf_token = os.environ.get("HUGGING_FACE_TOKEN")
if hf_token:
    login(token=hf_token)
else:
    print("Warning: HUGGING_FACE_TOKEN not set. Hub uploads will fail.")

Load and prepare datasets#

We’ll demonstrate all three supported dataset types.

print("Loading BNCI2014_001 dataset...")
# Load only subject 1 for this example
dataset = BNCI2014_001(subject_ids=[1])

print(f"  Number of recordings: {len(dataset.datasets)}")
print(f"  Channels: {len(dataset.datasets[0].raw.ch_names)}")
print(f"  Sampling frequency: {dataset.datasets[0].raw.info['sfreq']} Hz")

Loading BNCI2014_001 dataset...
  Number of recordings: 12
  Channels: 26
  Sampling frequency: 250.0 Hz

Example 1: WindowsDataset (Epoched data)#

Create epoched data using mne.Epochs (use_mne_epochs=True)

print("\n1. Creating WindowsDataset (epoched data)...")
windows_dataset = create_windows_from_events(
    concat_ds=BaseConcatDataset([dataset.datasets[0]]),
    trial_start_offset_samples=0,
    trial_stop_offset_samples=0,
    use_mne_epochs=True,  # Creates WindowsDataset with mne.Epochs
)

print(f"   Total windows: {len(windows_dataset)}")
print("   Dataset type: WindowsDataset (epoched)")

1. Creating WindowsDataset (epoched data)...
Using data from preloaded Raw for 48 events and 1000 original time points ...
0 bad epochs dropped
/home/runner/work/braindecode/braindecode/braindecode/datasets/base.py:705: UserWarning: The provided mne.Epochs object does not meet the requirements for fast epoch access. This may lead to slow data loading from disk. Consider preloading the epochs or checking the conditions in WindowsDataset._can_use_fast_get_epoch_from_raw.
  warnings.warn(
   Total windows: 48
   Dataset type: WindowsDataset (epoched)

Example 2: EEGWindowsDataset (Continuous with windowing)#

Create continuous raw data with windowing metadata (use_mne_epochs=False)

print("\n2. Creating EEGWindowsDataset (continuous with windowing)...")
eegwindows_dataset = create_windows_from_events(
    concat_ds=BaseConcatDataset([dataset.datasets[0]]),
    trial_start_offset_samples=0,
    trial_stop_offset_samples=0,
    use_mne_epochs=False,  # Creates EEGWindowsDataset with continuous raw
)

print(f"   Total windows: {len(eegwindows_dataset)}")
print("   Dataset type: EEGWindowsDataset (continuous)")

2. Creating EEGWindowsDataset (continuous with windowing)...
   Total windows: 48
   Dataset type: EEGWindowsDataset (continuous)

Example 3: RawDataset (Continuous without windowing)#

Use the original raw data without any windowing

print("\n3. Using RawDataset (continuous without windowing)...")
raw_dataset = BaseConcatDataset([dataset.datasets[0]])

print(f"   Number of recordings: {len(raw_dataset.datasets)}")
print("   Dataset type: RawDataset (continuous, no windows)")

3. Using RawDataset (continuous without windowing)...
   Number of recordings: 1
   Dataset type: RawDataset (continuous, no windows)

Upload datasets to Hugging Face Hub#

To upload a dataset, you need to:

Create a Hugging Face account at https://huggingface.co
Login using: huggingface-cli login
Choose a repository name (e.g., “username/dataset-name”)

Note: This example shows the code but doesn’t actually upload. Uncomment the code below to perform an actual upload.

print("\n" + "=" * 70)
print("UPLOADING TO HUGGING FACE HUB")
print("=" * 70)

# Skip Hub operations if no token is available (e.g., during docs build)
if not hf_token:
    print("\n⚠️  Skipping Hub upload examples (no HUGGING_FACE_TOKEN set)")
    print("To run this example with actual uploads, set HUGGING_FACE_TOKEN")
else:
    # Example 1: Upload WindowsDataset
    # ---------------------------------
    repo_id_windows = "braindecode/example_dataset-windows"

    print(f"\nUploading WindowsDataset to {repo_id_windows}...")
    url = windows_dataset.push_to_hub(
        repo_id=repo_id_windows,
        private=False,
    )
    print(f"✅ Uploaded to {url}!")

    # Example 2: Upload EEGWindowsDataset
    # ------------------------------------
    repo_id_eegwindows = "braindecode/example_dataset-eegwindows"

    print(f"\nUploading EEGWindowsDataset to {repo_id_eegwindows}...")
    url = eegwindows_dataset.push_to_hub(
        repo_id=repo_id_eegwindows,
        private=False,
    )
    print(f"✅ Uploaded to {url}!")

    # Example 3: Upload RawDataset
    # -----------------------------
    repo_id_raw = "braindecode/example_dataset-raw"

    print(f"\nUploading RawDataset to {repo_id_raw}...")
    url = raw_dataset.push_to_hub(
        repo_id=repo_id_raw,
        private=False,
    )
    print(f"✅ Uploaded to {url}!")

print("""
The example above demonstrates uploading to the Hugging Face Hub.
All datasets are converted to Zarr format (optimized for fast loading)
and uploaded with auto-generated dataset cards.

For your own datasets, replace the repo_id with your Hugging Face username.
""")

======================================================================
UPLOADING TO HUGGING FACE HUB
======================================================================

Uploading WindowsDataset to braindecode/example_dataset-windows...
Writing '/tmp/tmpogx8ystn/sourcedata/braindecode/dataset_description.json'...
Using data from preloaded Raw for 48 events and 1000 original time points ...

Recovering from metadata files:   0%|          | 0/12 [00:00<?, ?it/s]
Recovering from metadata files: 100%|██████████| 12/12 [00:00<00:00, 2464.10it/s]

---------- 2026-03-16 13:54:08 (0:00:00) ----------
Files:   hashed 1/12 (236.0/2.3M) | pre-uploaded: 0/0 (0.0/2.3M) (+12 unsure) | committed: 0/12 (0.0/2.3M) | ignored: 0
Workers: hashing: 2 | get upload mode: 0 | pre-uploading: 0 | committing: 0 | waiting: 0
---------------------------------------------------

Processing Files (0 / 0)      : |          |  0.00B /  0.00B

New Data Upload               : |          |  0.00B /  0.00B

  .../recording_0/data/c/0/0/0: 100%|██████████| 2.20MB / 2.20MB

  .../recording_0/data/c/0/0/0: 100%|██████████| 2.20MB / 2.20MB
Processing Files (1 / 1)      : 100%|██████████| 2.20MB / 2.20MB,   ???B/s

  .../recording_0/data/c/0/0/0: 100%|██████████| 2.20MB / 2.20MB

  .../recording_0/data/c/0/0/0: 100%|██████████| 2.20MB / 2.20MB
Processing Files (1 / 1)      : 100%|██████████| 2.20MB / 2.20MB,  0.00B/s

New Data Upload               : |          |  0.00B /  0.00B,  0.00B/s

  .../recording_0/data/c/0/0/0: 100%|██████████| 2.20MB / 2.20MB

Processing Files (0 / 0)      : |          |  0.00B /  0.00B

New Data Upload               : |          |  0.00B /  0.00B

  .../recording_0/data/c/0/0/0: 100%|██████████| 2.20MB / 2.20MB

  .../recording_0/data/c/0/0/0: 100%|██████████| 2.20MB / 2.20MB
Processing Files (1 / 1)      : 100%|██████████| 2.20MB / 2.20MB,   ???B/s

  .../recording_0/data/c/0/0/0: 100%|██████████| 2.20MB / 2.20MB
Processing Files (1 / 1)      : 100%|██████████| 2.20MB / 2.20MB,  0.00B/s

New Data Upload               : |          |  0.00B /  0.00B,  0.00B/s

  .../recording_0/data/c/0/0/0: 100%|██████████| 2.20MB / 2.20MB
✅ Uploaded to None!

Uploading EEGWindowsDataset to braindecode/example_dataset-eegwindows...
Writing '/tmp/tmp9gjjs02y/sourcedata/braindecode/dataset_description.json'...

Recovering from metadata files:   0%|          | 0/12 [00:00<?, ?it/s]
Recovering from metadata files: 100%|██████████| 12/12 [00:00<00:00, 2555.17it/s]

---------- 2026-03-16 13:54:20 (0:00:00) ----------
Files:   hashed 1/12 (237.0/4.4M) | pre-uploaded: 0/0 (0.0/4.4M) (+12 unsure) | committed: 0/12 (0.0/4.4M) | ignored: 0
Workers: hashing: 2 | get upload mode: 0 | pre-uploading: 0 | committing: 0 | waiting: 0
---------------------------------------------------

Processing Files (0 / 0)      : |          |  0.00B /  0.00B

New Data Upload               : |          |  0.00B /  0.00B

  ...rr/recording_0/data/c/0/0: 100%|██████████| 4.37MB / 4.37MB

  ...rr/recording_0/data/c/0/0: 100%|██████████| 4.37MB / 4.37MB
Processing Files (1 / 1)      : 100%|██████████| 4.37MB / 4.37MB,   ???B/s

  ...rr/recording_0/data/c/0/0: 100%|██████████| 4.37MB / 4.37MB

  ...rr/recording_0/data/c/0/0: 100%|██████████| 4.37MB / 4.37MB
Processing Files (1 / 1)      : 100%|██████████| 4.37MB / 4.37MB,  0.00B/s

New Data Upload               : |          |  0.00B /  0.00B,  0.00B/s

  ...rr/recording_0/data/c/0/0: 100%|██████████| 4.37MB / 4.37MB

Processing Files (0 / 0)      : |          |  0.00B /  0.00B

New Data Upload               : |          |  0.00B /  0.00B

  ...rr/recording_0/data/c/0/0: 100%|██████████| 4.37MB / 4.37MB

  ...rr/recording_0/data/c/0/0: 100%|██████████| 4.37MB / 4.37MB
Processing Files (1 / 1)      : 100%|██████████| 4.37MB / 4.37MB,   ???B/s

  ...rr/recording_0/data/c/0/0: 100%|██████████| 4.37MB / 4.37MB
Processing Files (1 / 1)      : 100%|██████████| 4.37MB / 4.37MB,  0.00B/s

New Data Upload               : |          |  0.00B /  0.00B,  0.00B/s

  ...rr/recording_0/data/c/0/0: 100%|██████████| 4.37MB / 4.37MB
✅ Uploaded to None!

Uploading RawDataset to braindecode/example_dataset-raw...
Writing '/tmp/tmp4a2x3c8k/sourcedata/braindecode/dataset_description.json'...

Recovering from metadata files:   0%|          | 0/10 [00:00<?, ?it/s]
Recovering from metadata files: 100%|██████████| 10/10 [00:00<00:00, 2545.86it/s]

---------- 2026-03-16 13:54:30 (0:00:00) ----------
Files:   hashed 1/10 (240.0/4.4M) | pre-uploaded: 0/0 (0.0/4.4M) (+10 unsure) | committed: 0/10 (0.0/4.4M) | ignored: 0
Workers: hashing: 2 | get upload mode: 0 | pre-uploading: 0 | committing: 0 | waiting: 0
---------------------------------------------------

Processing Files (0 / 0)      : |          |  0.00B /  0.00B

New Data Upload               : |          |  0.00B /  0.00B

  ...rr/recording_0/data/c/0/0: 100%|██████████| 4.37MB / 4.37MB

  ...rr/recording_0/data/c/0/0: 100%|██████████| 4.37MB / 4.37MB
Processing Files (1 / 1)      : 100%|██████████| 4.37MB / 4.37MB,   ???B/s

  ...rr/recording_0/data/c/0/0: 100%|██████████| 4.37MB / 4.37MB
Processing Files (1 / 1)      : 100%|██████████| 4.37MB / 4.37MB,  0.00B/s

New Data Upload               : |          |  0.00B /  0.00B,  0.00B/s

  ...rr/recording_0/data/c/0/0: 100%|██████████| 4.37MB / 4.37MB

Processing Files (0 / 0)      : |          |  0.00B /  0.00B

New Data Upload               : |          |  0.00B /  0.00B

  ...rr/recording_0/data/c/0/0: 100%|██████████| 4.37MB / 4.37MB

  ...rr/recording_0/data/c/0/0: 100%|██████████| 4.37MB / 4.37MB
Processing Files (1 / 1)      : 100%|██████████| 4.37MB / 4.37MB,   ???B/s

  ...rr/recording_0/data/c/0/0: 100%|██████████| 4.37MB / 4.37MB
Processing Files (1 / 1)      : 100%|██████████| 4.37MB / 4.37MB,  0.00B/s

New Data Upload               : |          |  0.00B /  0.00B,  0.00B/s

  ...rr/recording_0/data/c/0/0: 100%|██████████| 4.37MB / 4.37MB
✅ Uploaded to None!

The example above demonstrates uploading to the Hugging Face Hub.
All datasets are converted to Zarr format (optimized for fast loading)
and uploaded with auto-generated dataset cards.

For your own datasets, replace the repo_id with your Hugging Face username.

Zarr Format#

Datasets are uploaded in Zarr format, which provides:

Fastest random access (0.010 ms - critical for PyTorch training)
Excellent compression with blosc
Cloud-native, chunked storage
Ideal for datasets of all sizes
Based on comprehensive benchmarking with 1000 subjects

The format parameters (compression, compression_level) are optimized by default but can be customized if needed.

Download datasets from Hugging Face Hub#

Loading datasets from the Hub is simple and automatic!

print("\n" + "=" * 70)
print("DOWNLOADING FROM HUGGING FACE HUB")
print("=" * 70)

# Skip Hub downloads if no token (docs build)
if not hf_token:
    print("\n⚠️  Skipping Hub download examples (no HUGGING_FACE_TOKEN set)")
    print("To run this example with actual downloads, set HUGGING_FACE_TOKEN")
else:
    # Example 1: Download WindowsDataset
    # -----------------------------------
    public_repo_windows = "braindecode/example_dataset-windows"

    print(f"\nDownloading WindowsDataset from {public_repo_windows}...")
    loaded_windows = BaseConcatDataset.pull_from_hub(
        public_repo_windows,
        preload=True,  # Load into memory (False for lazy loading)
    )
    print("✅ Loaded WindowsDataset!")
    print(f"   Number of windows: {len(loaded_windows)}")

    # Example 2: Download EEGWindowsDataset
    # --------------------------------------
    public_repo_eeg = "braindecode/example_dataset-eegwindows"

    print(f"\nDownloading EEGWindowsDataset from {public_repo_eeg}...")
    loaded_eeg = BaseConcatDataset.pull_from_hub(
        public_repo_eeg,
        preload=True,
    )
    print("✅ Loaded EEGWindowsDataset!")
    print(f"   Number of windows: {len(loaded_eeg)}")

    # Example 3: Download RawDataset
    # -------------------------------
    public_repo_raw = "braindecode/example_dataset-raw"

    print(f"\nDownloading RawDataset from {public_repo_raw}...")
    loaded_raw = BaseConcatDataset.pull_from_hub(
        public_repo_raw,
        preload=True,
    )
    print("✅ Loaded RawDataset!")
    print(f"   Number of recordings: {len(loaded_raw.datasets)}")

print("""
The example above demonstrates downloading datasets from the Hub.
Datasets are automatically downloaded and cached locally.
Subsequent loads use the cache for faster access.
""")

======================================================================
DOWNLOADING FROM HUGGING FACE HUB
======================================================================

Downloading WindowsDataset from braindecode/example_dataset-windows...

Fetching 60 files:   0%|          | 0/60 [00:00<?, ?it/s]
Fetching 60 files:   5%|▌         | 3/60 [00:00<00:02, 28.24it/s]
Fetching 60 files:  10%|█         | 6/60 [00:00<00:05, 10.68it/s]
Fetching 60 files:  67%|██████▋   | 40/60 [00:00<00:00, 86.19it/s]
Fetching 60 files:  95%|█████████▌| 57/60 [00:00<00:00, 87.47it/s]
Fetching 60 files: 100%|██████████| 60/60 [00:00<00:00, 74.89it/s]
Adding metadata with 4 columns
48 matching events found
No baseline correction applied
0 projection items activated
✅ Loaded WindowsDataset!
   Number of windows: 48

Downloading EEGWindowsDataset from braindecode/example_dataset-eegwindows...

Fetching 22 files:   0%|          | 0/22 [00:00<?, ?it/s]
Fetching 22 files:  18%|█▊        | 4/22 [00:00<00:01, 13.62it/s]
Fetching 22 files:  27%|██▋       | 6/22 [00:00<00:01, 11.10it/s]
Fetching 22 files:  59%|█████▉    | 13/22 [00:00<00:00, 20.92it/s]
Fetching 22 files: 100%|██████████| 22/22 [00:00<00:00, 29.54it/s]
Creating RawArray with float64 data, n_channels=26, n_times=96735
    Range : 0 ... 96734 =      0.000 ...   386.936 secs
Ready.
✅ Loaded EEGWindowsDataset!
   Number of windows: 48

Downloading RawDataset from braindecode/example_dataset-raw...

Fetching 20 files:   0%|          | 0/20 [00:00<?, ?it/s]
Fetching 20 files:   5%|▌         | 1/20 [00:00<00:02,  8.74it/s]
Fetching 20 files:  20%|██        | 4/20 [00:00<00:01, 14.36it/s]
Fetching 20 files:  40%|████      | 8/20 [00:00<00:00, 22.99it/s]
Fetching 20 files:  60%|██████    | 12/20 [00:00<00:00, 24.60it/s]
Fetching 20 files: 100%|██████████| 20/20 [00:00<00:00, 34.37it/s]
Creating RawArray with float64 data, n_channels=26, n_times=96735
    Range : 0 ... 96734 =      0.000 ...   386.936 secs
Ready.
✅ Loaded RawDataset!
   Number of recordings: 1

The example above demonstrates downloading datasets from the Hub.
Datasets are automatically downloaded and cached locally.
Subsequent loads use the cache for faster access.

Using datasets with PyTorch DataLoader#

Datasets loaded from the Hub work seamlessly with PyTorch.

print("\n" + "=" * 70)
print("USING WITH PYTORCH DATALOADER")
print("=" * 70)

from torch.utils.data import DataLoader

# Create DataLoader
train_loader = DataLoader(
    windows_dataset,
    batch_size=32,
    shuffle=True,
    num_workers=0,  # Set to > 0 for parallel loading
)

print("\nDataLoader created:")
print("  Batch size: 32")
print(f"  Total batches: {len(train_loader)}")

# Iterate over a few batches
print("\n  Sample batches:")
for i, batch_data in enumerate(train_loader):
    if i >= 3:  # Show only 3 batches
        break
    # Handle both 2-tuple (X, y) and 3-tuple (X, y, inds) returns
    if len(batch_data) == 3:
        X_batch, y_batch, _ = batch_data
    else:
        X_batch, y_batch = batch_data
    print(
        f"    Batch {i + 1}: X shape={tuple(X_batch.shape)}, "
        f"y shape={tuple(y_batch.shape)}"
    )

print("""
This works the same way for datasets loaded from the Hub!
The Hub integration is fully compatible with PyTorch's training pipeline.
""")

======================================================================
USING WITH PYTORCH DATALOADER
======================================================================

DataLoader created:
  Batch size: 32
  Total batches: 2

  Sample batches:
Using data from preloaded Raw for 1 events and 1000 original time points ...
Using data from preloaded Raw for 1 events and 1000 original time points ...
Using data from preloaded Raw for 1 events and 1000 original time points ...
Using data from preloaded Raw for 1 events and 1000 original time points ...
Using data from preloaded Raw for 1 events and 1000 original time points ...
Using data from preloaded Raw for 1 events and 1000 original time points ...
Using data from preloaded Raw for 1 events and 1000 original time points ...
Using data from preloaded Raw for 1 events and 1000 original time points ...
Using data from preloaded Raw for 1 events and 1000 original time points ...
Using data from preloaded Raw for 1 events and 1000 original time points ...
Using data from preloaded Raw for 1 events and 1000 original time points ...
Using data from preloaded Raw for 1 events and 1000 original time points ...
Using data from preloaded Raw for 1 events and 1000 original time points ...
Using data from preloaded Raw for 1 events and 1000 original time points ...
Using data from preloaded Raw for 1 events and 1000 original time points ...
Using data from preloaded Raw for 1 events and 1000 original time points ...
Using data from preloaded Raw for 1 events and 1000 original time points ...
Using data from preloaded Raw for 1 events and 1000 original time points ...
Using data from preloaded Raw for 1 events and 1000 original time points ...
Using data from preloaded Raw for 1 events and 1000 original time points ...
Using data from preloaded Raw for 1 events and 1000 original time points ...
Using data from preloaded Raw for 1 events and 1000 original time points ...
Using data from preloaded Raw for 1 events and 1000 original time points ...
Using data from preloaded Raw for 1 events and 1000 original time points ...
Using data from preloaded Raw for 1 events and 1000 original time points ...
Using data from preloaded Raw for 1 events and 1000 original time points ...
Using data from preloaded Raw for 1 events and 1000 original time points ...
Using data from preloaded Raw for 1 events and 1000 original time points ...
Using data from preloaded Raw for 1 events and 1000 original time points ...
Using data from preloaded Raw for 1 events and 1000 original time points ...
Using data from preloaded Raw for 1 events and 1000 original time points ...
Using data from preloaded Raw for 1 events and 1000 original time points ...
    Batch 1: X shape=(32, 26, 1000), y shape=(32,)
Using data from preloaded Raw for 1 events and 1000 original time points ...
Using data from preloaded Raw for 1 events and 1000 original time points ...
Using data from preloaded Raw for 1 events and 1000 original time points ...
Using data from preloaded Raw for 1 events and 1000 original time points ...
Using data from preloaded Raw for 1 events and 1000 original time points ...
Using data from preloaded Raw for 1 events and 1000 original time points ...
Using data from preloaded Raw for 1 events and 1000 original time points ...
Using data from preloaded Raw for 1 events and 1000 original time points ...
Using data from preloaded Raw for 1 events and 1000 original time points ...
Using data from preloaded Raw for 1 events and 1000 original time points ...
Using data from preloaded Raw for 1 events and 1000 original time points ...
Using data from preloaded Raw for 1 events and 1000 original time points ...
Using data from preloaded Raw for 1 events and 1000 original time points ...
Using data from preloaded Raw for 1 events and 1000 original time points ...
Using data from preloaded Raw for 1 events and 1000 original time points ...
Using data from preloaded Raw for 1 events and 1000 original time points ...
    Batch 2: X shape=(16, 26, 1000), y shape=(16,)

This works the same way for datasets loaded from the Hub!
The Hub integration is fully compatible with PyTorch's training pipeline.

Advanced: Privacy, versioning, and collaboration#

The Hub provides powerful features for dataset management and collaboration. Every dataset repository on the Hub is backed by git-style versioning, so you can track changes, create branches/tags for specific dataset states, and collaborate with others through pull requests and reviews in the Hub UI.

Private datasets

For sensitive data or work-in-progress:

dataset.push_to_hub(
    repo_id="username/private-dataset",
    private=True
)

Best practices#

When sharing datasets on the Hub:

Include good documentation - The dataset card (README.md) is auto- generated but you can edit it on the Hub to add details about:
- Data collection methodology
- Preprocessing steps applied
- Known issues or limitations
- Citation information
Optimize compression if needed - The default blosc compression (level 5) provides an optimal balance. For very large datasets, experiment with compression_level parameter (0-9) to find the best trade-off between size and speed for your use case.

Test before sharing - Always test that your uploaded dataset can be downloaded and used correctly:

# Upload
dataset.push_to_hub("braindecode/example_dataset")

# Test download
test_dataset = BaseConcatDataset.pull_from_hub("braindecode/example_dataset")
assert len(test_dataset) == len(dataset)

Consider privacy - Ensure you have permission to share the data and that personal information has been properly anonymized.

print("\n" + "=" * 70)
print("SUMMARY")
print("=" * 70)
print("""
Hugging Face Hub integration supports three dataset types:

✓ WindowsDataset - Epoched data (mne.Epochs)
✓ EEGWindowsDataset - Continuous with windowing metadata
✓ RawDataset - Continuous without windowing

Benefits:
✓ Share datasets with the research community
✓ Version control with git-like versioning
✓ Collaborate on dataset curation
✓ Access datasets from anywhere
✓ Automatic caching for faster repeated loads
✓ Optimized Zarr format for fast training

For more information:
- Hugging Face Hub: https://huggingface.co
- Braindecode docs: https://braindecode.org
- Hub docs: https://huggingface.co/docs/hub
""")

======================================================================
SUMMARY
======================================================================

Hugging Face Hub integration supports three dataset types:

✓ WindowsDataset - Epoched data (mne.Epochs)
✓ EEGWindowsDataset - Continuous with windowing metadata
✓ RawDataset - Continuous without windowing

Benefits:
✓ Share datasets with the research community
✓ Version control with git-like versioning
✓ Collaborate on dataset curation
✓ Access datasets from anywhere
✓ Automatic caching for faster repeated loads
✓ Optimized Zarr format for fast training

For more information:
- Hugging Face Hub: https://huggingface.co
- Braindecode docs: https://braindecode.org
- Hub docs: https://huggingface.co/docs/hub

Total running time of the script: (0 minutes 40.361 seconds)

Estimated memory usage: 1300 MB

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