Upload BNCI2014_001 WindowsDataset (epoched)

README.md

@@ -4,105 +4,105 @@ tags:
 - eeg
 - neuroscience
 - brain-computer-interface
-- bids
+- deep-learning
 license: unknown
 ---
 
 # EEG Dataset
 
-This dataset was created using [braindecode](https://braindecode.org), a library for deep learning with EEG/MEG/ECoG signals.
+This dataset was created using [braindecode](https://braindecode.org), a deep
+learning library for EEG/MEG/ECoG signals.
 
 ## Dataset Information
 
 | Property | Value |
-|---|---:|
-| Number of recordings | 1 |
-| Dataset type | Windowed (from Epochs object) |
-| Number of channels | 26 |
+|----------|------:|
+| Recordings | 1 |
+| Type | Windowed (from Epochs object) |
+| Channels | 26 |
 | Sampling frequency | 250 Hz |
-| Number of windows / samples | 48 |
-| Total size | 0.03 MB |
-| Storage format | zarr |
-| BIDS compatible | Yes |
+| Windows/samples | 48 |
+| Size | 0.03 MB |
+| Format | zarr |
 
-## Usage
+## Quick Start
 
-To load this dataset::
-
-    .. code-block:: python
+```python
+from braindecode.datasets import BaseConcatDataset
 
-        from braindecode.datasets import BaseConcatDataset
+# Load from Hugging Face Hub
+dataset = BaseConcatDataset.pull_from_hub("username/dataset-name")
 
-        # Load dataset 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
+```
 
-        # Access data
-        X, y, metainfo = dataset[0]
-        # X: EEG data (n_channels, n_times)
-        # y: label/target
-        # metainfo: window indices
+## Training with PyTorch
 
-## Using with PyTorch DataLoader
+```python
+from torch.utils.data import DataLoader
 
-::
+loader = DataLoader(dataset, batch_size=32, shuffle=True, num_workers=4)
 
-    from torch.utils.data import DataLoader
+for X, y, metainfo in loader:
+    # X: [batch_size, n_channels, n_times]
+    # y: [batch_size]
+    pass  # Your training code
+```
 
-    # Create DataLoader for training
-    train_loader = DataLoader(
-        dataset,
-        batch_size=32,
-        shuffle=True,
-        num_workers=4
-    )
+## BIDS-inspired Structure
 
-    # Training loop
-    for X, y, metainfo in train_loader:
-        # X shape: [batch_size, n_channels, n_times]
-        # y shape: [batch_size]
-        # metainfo shape: [batch_size, 2] (start and end indices)
-        # Process your batch...
+This dataset uses a **BIDS-inspired** organization. Metadata files follow BIDS
+conventions, while data is stored in Zarr format for efficient deep learning.
 
-## BIDS-like Structure
+**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
 
-This dataset follows a BIDS derivatives-like structure for compatibility with
-neuroimaging tools while maintaining efficiency for deep learning:
+**Data storage:**
+- `dataset.zarr/` - Zarr format (optimized for random access)
 
 ```
-derivatives/braindecode/
-├── dataset_description.json    # BIDS dataset description
-├── participants.tsv            # Subject-level metadata
-├── dataset.zarr/               # Main data (optimized for training)
+sourcedata/braindecode/
+├── dataset_description.json
+├── participants.tsv
+├── dataset.zarr/
 └── sub-<label>/
     └── eeg/
-        ├── *_events.tsv        # Trial/window events
-        ├── *_channels.tsv      # Channel information
-        └── *_eeg.json          # Recording metadata
+        ├── *_events.tsv
+        ├── *_channels.tsv
+        └── *_eeg.json
 ```
 
-### Accessing BIDS Metadata
-
-After loading the dataset, BIDS metadata is available:
+### Accessing Metadata
 
 ```python
-# Access participants info
+# Participants info
 if hasattr(dataset, "participants"):
     print(dataset.participants)
 
-# Access events for a recording
+# Events for a recording
 if hasattr(dataset.datasets[0], "bids_events"):
     print(dataset.datasets[0].bids_events)
 
-# Access channel info
+# Channel info
 if hasattr(dataset.datasets[0], "bids_channels"):
     print(dataset.datasets[0].bids_channels)
 ```
 
-## Dataset Format
+## Why Zarr?
 
-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
+Zarr format provides:
+- **Fast random access** - Critical for efficient DataLoader sampling
+- **Compression** - Reduced storage with minimal read overhead
+- **Cloud-native** - Works with remote storage (S3, GCS, etc.)
+
+---
 
-For more information about braindecode, visit: https://braindecode.org
+*Created with [braindecode](https://braindecode.org)*

format_info.json

@@ -1,6 +1,5 @@
 {
   "format": "zarr",
-  "bids_compatible": true,
   "pipeline_name": "braindecode",
   "compression": "blosc",
   "compression_level": 5,