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library_name: transformers
pipeline_tag: feature-extraction
license: apache-2.0
language:
- en
tags:
- eeg
- neuroscience
- foundation-model
- masked-autoencoder
- representation-learning
- pytorch
- custom_code
---
# MANAS-2
MANAS-2 is a transformer-based foundation model for EEG representation learning, developed by [MannasAI](https://huggingface.co/MannasAI). It combines **Raw-Band Hybrid (RBH)** masked autoencoding with **Constrained Reconstruction (ConRec)** to learn temporal and spectral features from EEG.
## Model Details
MANAS-2 has **92.8M parameters**, 22 transformer layers, and 512-dimensional embeddings. It processes EEG sampled at **200 Hz** using one-second patches with a 0.1-second overlap and spatiotemporal positional encoding. Channel count and signal length can vary.
During pretraining, RBH uses waveform and spectral reconstruction, while ConRec regularizes local RMS-energy continuity in reconstructed waveforms. Pretraining uses unlabeled clinical EEG from internal sources, TUH EEG, and I-CARE. This release contains the encoder for feature extraction and fine-tuning.
**Paper:** [MANAS-2: Constrained Reconstruction for EEG Foundation Models](https://arxiv.org/abs/2609.13717)
## Uses
Install the dependencies:
```bash
pip install "torch>=2.2" "transformers>=4.57,<5" "safetensors>=0.4"
```
Extract embeddings using the included electrode-position lookup:
```python
import torch
from transformers import AutoModel
model = AutoModel.from_pretrained(
"MannasAI/MANAS-2", trust_remote_code=True,
).eval()
# Example: 10 seconds of EEG at 200 Hz.
electrode_names = ["Fz", "Cz", "Pz"]
eeg = torch.randn(1, 3, 2000) # [batch, channels, samples]
eeg = (eeg - eeg.mean(-1, keepdim=True)) / (
eeg.std(-1, keepdim=True, correction=1) + 1e-6
)
positions = model.get_channel_positions(electrode_names)
positions = positions.unsqueeze(0).expand(eeg.size(0), -1, -1)
with torch.inference_mode():
features = model(eeg, positions) # [1, 11, 3, 512]
embedding = features.mean(dim=(1, 2)) # [1, 512]
```
For real EEG, filter and resample to 200 Hz, then normalize each channel within each window as above. Electrode names must match the signal's channel order; the lookup returns coordinates in centimeters. The model expects preprocessed inputs and returns **[batch, patches, channels, 512]** features.
## License
Model weights and code are released under [Apache-2.0](LICENSE).
## Citation
```bibtex
@misc{kulkarni2026manas2,
title={MANAS-2: Constrained Reconstruction for EEG Foundation Models},
author={Arvasu Kulkarni and Aditya Ray Mishra and Mahir Jain and Parshva Runwal and Lakshya Saini and Siddharth Panwar and Sandeep Singh},
year={2026},
eprint={2609.13717},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2609.13717}
}
```
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