paris-cit-university-0f03658f·1 events·first seen Aliases: Paris Cité University
Researchers from Meta and several French and Spanish institutions released Brain2Qwerty v2, a non-invasive brain-computer interface system that decodes magnetoencephalography (MEG) signals into text using a CNN/conformer encoder, a word-aligner, and a fine-tuned Qwen3-4B language model with per-subject LoRA adapters. The system achieves a 39% word error rate on 9 subjects, down from 43% in v1, trained on 90 hours of MEG recordings. A notable finding is that cross-subject training substantially outperforms single-subject training, suggesting a data-scaling dynamic analogous to LLM pretraining. Training code and v1 data have been open-sourced.