Upload BNCI2014_001 WindowsDataset (epoched)

README.md

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+---
+tags:
+- braindecode
+- eeg
+- neuroscience
+- brain-computer-interface
+- deep-learning
+license: unknown
+---
+
+# EEG Dataset
+
+This dataset was created using [braindecode](https://braindecode.org), a deep
+learning library for EEG/MEG/ECoG signals.
+
+## Dataset Information
+
+| Property | Value |
+|----------|------:|
+| Recordings | 1 |
+| Type | Windowed (from Epochs object) |
+| Channels | 26 |
+| Sampling frequency | 250 Hz |
+| Total duration | 0:03:11 |
+| Windows/samples | 48 |
+| Size | 0.03 MB |
+| Format | zarr |
+
+## Quick Start
+
+```python
+from braindecode.datasets import BaseConcatDataset
+
+# Load from Hugging Face Hub
+dataset = BaseConcatDataset.pull_from_hub("username/dataset-name")
+
+# Access a sample
+X, y, metainfo = dataset[0]
+# X: EEG data [n_channels, n_times]
+# y: target label
+# metainfo: window indices
+```
+
+## Training with PyTorch
+
+```python
+from torch.utils.data import DataLoader
+
+loader = DataLoader(dataset, batch_size=32, shuffle=True, num_workers=4)
+
+for X, y, metainfo in loader:
+    # X: [batch_size, n_channels, n_times]
+    # y: [batch_size]
+    pass  # Your training code
+```
+
+## BIDS-inspired Structure
+
+This dataset uses a **BIDS-inspired** organization. Metadata files follow BIDS
+conventions, while data is stored in Zarr format for efficient deep learning.
+
+**BIDS-style metadata:**
+- `dataset_description.json` - Dataset information
+- `participants.tsv` - Subject metadata
+- `*_events.tsv` - Trial/window events
+- `*_channels.tsv` - Channel information
+- `*_eeg.json` - Recording parameters
+
+**Data storage:**
+- `dataset.zarr/` - Zarr format (optimized for random access)
+
+```
+sourcedata/braindecode/
+├── dataset_description.json
+├── participants.tsv
+├── dataset.zarr/
+└── sub-<label>/
+    └── eeg/
+        ├── *_events.tsv
+        ├── *_channels.tsv
+        └── *_eeg.json
+```
+
+### Accessing Metadata
+
+```python
+# Participants info
+if hasattr(dataset, "participants"):
+    print(dataset.participants)
+
+# Events for a recording
+if hasattr(dataset.datasets[0], "bids_events"):
+    print(dataset.datasets[0].bids_events)
+
+# Channel info
+if hasattr(dataset.datasets[0], "bids_channels"):
+    print(dataset.datasets[0].bids_channels)
+```
+
+---
+
+*Created with [braindecode](https://braindecode.org)*

format_info.json

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+{
+  "format": "zarr",
+  "pipeline_name": "braindecode",
+  "compression": "blosc",
+  "compression_level": 5,
+  "braindecode_version": "1.3.0",
+  "n_recordings": 1,
+  "total_samples": 48,
+  "total_size_mb": 0.03
+}