Add paper link, GitHub link, task category and sample usage

README.md

@@ -1,12 +1,68 @@
 ---
-license: cc-by-nc-4.0
 language:
-  - en
+- en
+license: cc-by-nc-4.0
+task_categories:
+- other
 ---
 
 # LibriBrain (Sherlock Holmes 1–7)
 
-This repository contains the LibriBrain data organised by book: MEG recordings (`.h5`), event annotations (`.tsv`), and the audiobook stimulus audio (`.wav`).
+[Paper](https://huggingface.co/papers/2506.02098) | [Code](https://github.com/neural-processing-lab/pnpl)
+
+This repository contains the LibriBrain data organised by book: MEG recordings (`.h5`), event annotations (`.tsv`), and the audiobook stimulus audio (`.wav`). 
+
+LibriBrain was first open-sourced as part of the [2025 PNPL Competition](https://libribrain.com/).
+
+In addition, LibriBrain is used as a fine-tuning dataset in the paper ["MEG-XL: Data-Efficient Brain-to-Text via Long-Context Pre-Training"](https://huggingface.co/papers/2602.02494) to evaluate word decoding from brain data.
+
+## Sample Usage
+The easiest way to get started with the dataset is using the [pnpl Python library](https://github.com/neural-processing-lab/pnpl). There, the following two datasets are available:
+
+### LibriBrainSpeech
+This wraps the LibriBrain dataset for use in speech detection problems.
+```python
+from pnpl.datasets import LibriBrainSpeech
+
+speech_example_data = LibriBrainSpeech(
+    data_path="./data/",
+    partition="train"
+)
+
+sample_data, label = speech_example_data[0]
+
+# Print out some basic info about the sample
+print("Sample data shape:", sample_data.shape)
+print("Label shape:", label.shape)
+```
+
+### LibriBrainPhoneme
+This wraps the LibriBrain dataset for use in phoneme classification problems.
+```python
+from pnpl.datasets import LibriBrainPhoneme
+
+phoneme_example_data = LibriBrainPhoneme(
+    data_path="./data/",
+    partition="train"
+)
+sample_data, label = phoneme_example_data[0]
+
+# Print out some basic info about the sample
+print("Sample data shape:", sample_data.shape)
+print("Label shape:", label.shape)
+```
+
+### Usage in MEG-XL
+To fine-tune the MEG-XL model on the LibriBrain dataset for word decoding, you can use the following command from the [official repository](https://github.com/neural-processing-lab/MEG-XL):
+
+```bash
+python -m brainstorm.evaluate_criss_cross_word_classification \
+  --config-name=eval_criss_cross_word_classification_libribrain \
+  model.criss_cross_checkpoint=/path/to/your/checkpoint.ckpt
+```
+
+Note: For the MEG-XL repo, you will need to adjust the dataset paths in the configuration files to point to your local download of the data. The pnpl library includes automatic downloads from HuggingFace.
+
 
 ## Repository structure
 
@@ -50,6 +106,8 @@ sox "INPUT_FROM_LIBRIVOX.mp3" -c 1 -r 22050 -b 16 "OUTPUT.wav"
 
 ### Citation
 
+If you use this dataset, please cite the LibriBrain paper:
+
 ```bibtex
 @article{ozdogan2025libribrain,
   author    = {Özdogan, Miran and Landau, Gilad and Elvers, Gereon and Jayalath, Dulhan and Somaiya, Pratik and Mantegna, Francesco and Woolrich, Mark and Parker Jones, Oiwi},
@@ -59,3 +117,14 @@ sox "INPUT_FROM_LIBRIVOX.mp3" -c 1 -r 22050 -b 16 "OUTPUT.wav"
   url       = {https://arxiv.org/abs/2506.02098},
 }
 ```
+
+If you use this data with the MEG-XL framework, please also cite:
+
+```bibtex
+@article{jayalath2026megxl,
+  title={{MEG-XL}: Data-Efficient Brain-to-Text via Long-Context Pre-Training},
+  author={Jayalath, Dulhan and Jones, Oiwi Parker},
+  journal={arXiv preprint arXiv:2602.02494},
+  year={2026}
+}
+```