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--- |
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tags: |
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- braindecode |
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- eeg |
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- neuroscience |
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- brain-computer-interface |
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- bids |
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license: unknown |
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--- |
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# EEG Dataset |
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This dataset was created using [braindecode](https://braindecode.org), a library for deep learning with EEG/MEG/ECoG signals. |
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## Dataset Information |
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| Property | Value | |
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|---|---:| |
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| Number of recordings | 1 | |
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| Dataset type | Windowed (from Raw object) | |
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| Number of channels | 26 | |
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| Sampling frequency | 250 Hz | |
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| Number of windows / samples | 48 | |
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| Total size | 19.22 MB | |
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| Storage format | zarr | |
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| BIDS compatible | Yes | |
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## Usage |
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To load this dataset:: |
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.. code-block:: python |
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from braindecode.datasets import BaseConcatDataset |
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# Load dataset from Hugging Face Hub |
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dataset = BaseConcatDataset.pull_from_hub("username/dataset-name") |
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# Access data |
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X, y, metainfo = dataset[0] |
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# X: EEG data (n_channels, n_times) |
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# y: label/target |
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# metainfo: window indices |
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## Using with PyTorch DataLoader |
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:: |
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from torch.utils.data import DataLoader |
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# Create DataLoader for training |
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train_loader = DataLoader( |
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dataset, |
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batch_size=32, |
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shuffle=True, |
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num_workers=4 |
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) |
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# Training loop |
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for X, y, metainfo in train_loader: |
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# X shape: [batch_size, n_channels, n_times] |
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# y shape: [batch_size] |
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# metainfo shape: [batch_size, 2] (start and end indices) |
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# Process your batch... |
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## BIDS-like Structure |
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This dataset follows a BIDS derivatives-like structure for compatibility with |
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neuroimaging tools while maintaining efficiency for deep learning: |
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``` |
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derivatives/braindecode/ |
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├── dataset_description.json # BIDS dataset description |
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├── participants.tsv # Subject-level metadata |
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├── dataset.zarr/ # Main data (optimized for training) |
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└── sub-<label>/ |
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└── eeg/ |
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├── *_events.tsv # Trial/window events |
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├── *_channels.tsv # Channel information |
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└── *_eeg.json # Recording metadata |
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``` |
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### Accessing BIDS Metadata |
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After loading the dataset, BIDS metadata is available: |
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```python |
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# Access participants info |
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if hasattr(dataset, "participants"): |
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print(dataset.participants) |
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# Access events for a recording |
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if hasattr(dataset.datasets[0], "bids_events"): |
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print(dataset.datasets[0].bids_events) |
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# Access channel info |
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if hasattr(dataset.datasets[0], "bids_channels"): |
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print(dataset.datasets[0].bids_channels) |
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``` |
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## Dataset Format |
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This dataset is stored in **Zarr** format, optimized for: |
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- Fast random access during training (critical for PyTorch DataLoader) |
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- Efficient compression with blosc |
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- Cloud-native storage compatibility |
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For more information about braindecode, visit: https://braindecode.org |
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