---
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

- **Number of recordings**: 1
- **Number of channels**: 26
- **Sampling frequency**: 250.0 Hz
- **Data type**: Continuous (Raw)
- **Number of windows**: 96735
- **Total size**: 19.23 MB
- **Storage format**: zarr

## Usage

To load this dataset:

```python
from braindecode.datasets import BaseConcatDataset

# Load dataset from Hugging Face Hub
dataset = BaseConcatDataset.from_pretrained("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

```python
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, _ in train_loader:
    # X shape: [batch_size, n_channels, n_times]
    # y shape: [batch_size]
    # 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