# Research | Setu Labs Source: https://setu-labs.co/research.html > Setu Labs research: open EEG models, silent-speech decoding and the architecture behind the next human interface. # What we are working on. Our research follows one belief: the interface between people and machines is a learning problem. The sensor sets the ceiling. What decides the rest is how fast a model can come to know one person. New release · v0.1.0 ## SETU Neural EEG Trained models for the next human interface. Our EEG model suite and the software behind it, released in the open. [Read the release →](https://setu-labs.co/research-neural-eeg.html)[Hugging Face ↗](https://huggingface.co/arushsinghal/setu-neural-eeg)[GitHub ↗](https://github.com/arushsinghal/setu-models) **919**inference checkpoints **5.86 GB**of trained artifacts **30.4%**held-out accuracy on five commands, against 20% chance **15**calibrated participants, 750 held-out trials ## All research [**Model release**v0.1.0 · EEG ### SETU Neural EEG: trained models for the next human interface. Task-trained neural networks, fine-tuned EEG encoders and command classifiers, with open-source training, adaptation and evaluation code. 919 inference checkpoints · 5.86 GB of trained artifacts · Open source ](https://setu-labs.co/research-neural-eeg.html) [**Technical summary**EMG ### Silent speech, from muscle to text. SETU-EMG decodes deliberately mouthed speech from eight channels of facial muscle activity, with no audio at inference time. 16.16% word error rate on the benchmark test partition. Technical summary. Internal results, not yet peer reviewed. ](https://setu-labs.co/research-emg.html) [**Architecture overview**EEG ### Signal and position, together. SetuMontageNet combines measured EEG with electrode locations to study motor-imagery decoding. Missing channels stay masked; a separate policy turns decoder outputs into computer actions. Architecture overview. Performance evaluation and live-hardware studies will be reported separately. ](https://setu-labs.co/research-eeg.html)