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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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- deep-learning |
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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 deep |
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learning library for EEG/MEG/ECoG signals. |
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## Dataset Information |
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| Property | Value | |
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|----------|------:| |
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| Recordings | 1 | |
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| Type | Windowed (from Epochs object) | |
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| Channels | 26 | |
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| Sampling frequency | 250 Hz | |
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| Total duration | 0:03:11 | |
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| Windows/samples | 48 | |
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| Size | 0.03 MB | |
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| Format | zarr | |
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## Quick Start |
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```python |
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from braindecode.datasets import BaseConcatDataset |
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# Load from Hugging Face Hub |
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dataset = BaseConcatDataset.pull_from_hub("username/dataset-name") |
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# Access a sample |
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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: target label |
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# metainfo: window indices |
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``` |
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## Training with PyTorch |
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```python |
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from torch.utils.data import DataLoader |
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loader = DataLoader(dataset, batch_size=32, shuffle=True, num_workers=4) |
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for X, y, metainfo in loader: |
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# X: [batch_size, n_channels, n_times] |
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# y: [batch_size] |
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pass # Your training code |
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``` |
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## BIDS-inspired Structure |
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This dataset uses a **BIDS-inspired** organization. Metadata files follow BIDS |
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conventions, while data is stored in Zarr format for efficient deep learning. |
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**BIDS-style metadata:** |
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- `dataset_description.json` - Dataset information |
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- `participants.tsv` - Subject metadata |
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- `*_events.tsv` - Trial/window events |
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- `*_channels.tsv` - Channel information |
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- `*_eeg.json` - Recording parameters |
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**Data storage:** |
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- `dataset.zarr/` - Zarr format (optimized for random access) |
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``` |
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sourcedata/braindecode/ |
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├── dataset_description.json |
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├── participants.tsv |
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├── dataset.zarr/ |
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└── sub-<label>/ |
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└── eeg/ |
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├── *_events.tsv |
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├── *_channels.tsv |
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└── *_eeg.json |
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``` |
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### Accessing Metadata |
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```python |
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# Participants info |
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if hasattr(dataset, "participants"): |
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print(dataset.participants) |
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# 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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# 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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--- |
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*Created with [braindecode](https://braindecode.org)* |
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