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---
tags:
- braindecode
- eeg
- neuroscience
- brain-computer-interface
- bids
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 |
| 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:
```python
# 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
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