metadata
tags:
- braindecode
- eeg
- neuroscience
- brain-computer-interface
license: unknown
EEG Dataset
This dataset was created using braindecode, 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:
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
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