README.md

# 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.