|
|
--- |
|
|
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**: Windowed (from Epochs object) |
|
|
- **Number of windows**: 48 |
|
|
- **Total size**: 0.04 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 |
|
|
|
