Kkuntal990/test-eegwindows-refactored

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 Raw object)
• Number of windows: 48
• Total size: 19.23 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