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+# LibriBrain MEG Preprocessed Dataset
+
+Preprocessed magnetoencephalography (MEG) recordings with phoneme labels from the LibriBrain dataset, optimized for fast loading during machine learning model training.
+
+This dataset was created for the [LibriBrain 2025 Competition](https://neural-processing-lab.github.io/2025-libribrain-competition/) (now concluded).
+
+## Dataset Overview
+
+### MEG Recording Specifications
+- **Channels**: 306 total (102 magnetometers + 204 gradiometers)
+- **Sampling Rate**: 250 Hz
+- **Duration**: ~52 hours of recordings
+- **Subject**: Single English speaker listening to Sherlock Holmes audiobooks
+- **Phoneme Instances**: ~1.5 million
+
+### Phoneme Inventory
+39 ARPAbet phonemes with position encoding:
+- **Vowels** (15): aa, ae, ah, ao, aw, ay, eh, er, ey, ih, iy, ow, oy, uh, uw
+- **Consonants** (24): b, ch, d, dh, f, g, hh, jh, k, l, m, n, ng, p, r, s, sh, t, th, v, w, y, z, zh
+- **Special**: oov (out-of-vocabulary)
+
+Position markers: B (beginning), I (inside), E (end), S (singleton)
+
+### Signal Processing
+All MEG data has been preprocessed through the following pipeline:
+1. Bad channel removal
+2. Signal Space Separation (SSS) for noise reduction
+3. Notch filtering for powerline noise removal
+4. Bandpass filtering (0.1-125 Hz)
+5. Downsampling to 250 Hz
+
+## Preprocessing and Grouping
+
+This dataset contains pre-grouped and averaged MEG samples for significantly faster data loading during training. Instead of grouping samples on-the-fly (which is computationally expensive), samples have been pre-grouped at various levels.
+
+### Available Grouping Configurations
+- `grouped_5`: 5 samples averaged together
+- `grouped_10`: 10 samples averaged together
+- `grouped_15`: 15 samples averaged together
+- `grouped_20`: 20 samples averaged together
+- `grouped_25`: 25 samples averaged together
+- `grouped_30`: 30 samples averaged together
+- `grouped_35`: 35 samples averaged together (partial - train only)
+- `grouped_45`: 45 samples averaged together
+- `grouped_50`: 50 samples averaged together
+- `grouped_55`: 55 samples averaged together
+- `grouped_60`: 60 samples averaged together
+- `grouped_100`: 100 samples averaged together
+
+Each configuration contains:
+- `train_grouped.h5`: Training data
+- `validation_grouped.h5`: Validation data
+- `test_grouped.h5`: Test data
+- `paths.yaml`: File path references
+
+### Why Use Grouped Data?
+- **Faster Loading**: Pre-computed grouping eliminates runtime averaging overhead
+- **Memory Efficient**: Smaller file sizes for higher grouping levels
+- **Flexible**: Choose grouping level based on your accuracy vs. speed requirements
+- **Standardized**: Consistent preprocessing across all configurations
+
+## Installation
+
+This dataset requires the modified pnpl library for loading:
+
+```bash
+pip install git+https://github.com/September-Labs/pnpl.git
+```
+
+## Usage
+
+```python
+from pnpl.datasets import GroupedDataset
+
+# Load preprocessed data with 100-sample grouping
+train_dataset = GroupedDataset(
+    preprocessed_path="data/grouped_100/train_grouped.h5",
+    load_to_memory=True  # Optional: load entire dataset to memory for faster access
+)
+
+val_dataset = GroupedDataset(
+    preprocessed_path="data/grouped_100/validation_grouped.h5",
+    load_to_memory=True
+)
+
+# Get a sample
+sample = train_dataset[0]
+meg_data = sample['meg']        # Shape: (306, time_points)
+phoneme_label = sample['phoneme']  # Phoneme class index
+
+# Use with PyTorch DataLoader
+from torch.utils.data import DataLoader
+
+dataloader = DataLoader(
+    train_dataset,
+    batch_size=32,
+    shuffle=True,
+    num_workers=4
+)
+```
+
+## Data Structure
+
+```
+data/
+├── grouped_5/
+│   ├── train_grouped.h5
+│   ├── validation_grouped.h5
+│   ├── test_grouped.h5
+│   └── paths.yaml
+├── grouped_10/
+│   ├── train_grouped.h5
+│   ├── validation_grouped.h5
+│   ├── test_grouped.h5
+│   └── paths.yaml
+├── ...
+└── grouped_100/
+    ├── train_grouped.h5
+    ├── validation_grouped.h5
+    ├── test_grouped.h5
+    └── paths.yaml
+```
+
+## File Sizes
+
+| Grouping | Train | Validation | Test | Total |
+|----------|-------|------------|------|-------|
+| grouped_5 | 45.6 GB | 425 MB | 456 MB | ~47 GB |
+| grouped_10 | 22.8 GB | 213 MB | 228 MB | ~24 GB |
+| grouped_20 | 11.4 GB | 106 MB | 114 MB | ~12 GB |
+| grouped_50 | 4.6 GB | 37 MB | 42 MB | ~4.7 GB |
+| grouped_100 | 2.3 GB | 19 MB | 21 MB | ~2.4 GB |
+
+## Dataset Splits
+
+- **Train**: 88 sessions (~51 hours)
+- **Validation**: 1 session (~0.36 hours)
+- **Test**: 1 session (~0.38 hours)
+
+## Citation
+
+If you use this dataset, please cite the LibriBrain competition:
+
+```bibtex
+@misc{libribrain2025,
+  title={LibriBrain: A Dataset for Speech Decoding from Brain Signals},
+  author={Neural Processing Lab},
+  year={2025},
+  url={https://neural-processing-lab.github.io/2025-libribrain-competition/}
+}
+```
+
+## License
+
+Please refer to the original LibriBrain dataset license terms.
+
+## Acknowledgments
+
+This preprocessed version was created to facilitate faster training for the LibriBrain 2025 Competition. The original dataset and competition were organized by the Neural Processing Lab.