# Meta开源脑机接口系统Brain2Qwerty v2，无需手术词准确率达78%

- 来源：Rohan Paul (@rohanpaul_ai)
- 发布时间：2026-07-01 07:21
- AIHOT 分数：51
- AIHOT 链接：https://aihot.virxact.com/items/cmr1a6ukc0101slnli2v517ig
- 原文链接：https://x.com/rohanpaul_ai/status/2072098321670553959

## AI 摘要

Meta开源非侵入式脑机接口系统Brain2Qwerty v2，通过读取MEG头盔采集的脑信号实现文字输出，无需植入电极。9名志愿者每人录入约10小时、共约2.2万句神经活动数据。系统平均词准确率61%，最强参与者达78%；超50%句子解码误差不超过1个词。v2版本联合映射脑信号到字符、词汇及完整句义，深度学习直接从原始信号学习模式，再经微调LLM利用上下文修正错误。相比早期非侵入方法8%的准确率显著提升，且准确率随训练数据量增加而提高。

## 正文

Meta open-sourced a brain-to-text system that reaches 78% word accuracy without surgery.

Brain2Qwerty v2 converts non-invasive brain recordings into text with 61% average word accuracy and 78% for its strongest participant.

The system reads MEG signals from a helmet， not electrodes placed inside brain tissue. 9 volunteers typed about 22，000 sentences while researchers recorded 10 hours of neural activity each.

Brain2Qwerty v1 mostly mapped brain signals to single typed characters. It tries to recover characters， words， and full sentence meaning together. The system studies those brain signals and tries to turn them into the words you wanted to type.

- 61% average word accuracy across all participants
- 78% word accuracy for the top participant
- 50%+ of sentences decoded with no more than 1 word error

Performance improves as the data pile grows

Raw brain signals are messy because many mental and physical processes fire at once. Deep learning handles that mess by learning patterns directly from the original recordings.

A fine-tuned LLM then uses language context to repair likely word and sentence errors. This explains why the system beats earlier non-invasive methods reporting 8% word accuracy.

More than half of sentences from the strongest participant had one word error or less. Accuracy also improved as training data grew， suggesting more recordings may close more of the gap.