| --- |
| tags: |
| - braindecode |
| - eeg |
| - neuroscience |
| - brain-computer-interface |
| license: unknown |
| --- |
| |
| # EEG Dataset |
|
|
| This dataset was created using [braindecode](https://braindecode.org), a library for deep learning with EEG/MEG/ECoG signals. |
|
|
| ## Dataset Information |
|
|
| | Property | Value | |
| |---|---:| |
| | Number of recordings | 1 | |
| | Dataset type | Windowed (from Raw object) | |
| | Number of channels | 26 | |
| | Sampling frequency | 250 Hz | |
| | Number of windows / samples | 48 | |
| | Total size | 19.22 MB | |
| | Storage format | zarr | |
|
|
| ## 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... |
| |
| ## 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 |
|
|
