Meta recently open-sourced Brain2Qwerty v2, a noninvasive Brain – Computer Interface ( BCI ) that can decode sentences from thoughts using electroencephalography ( EEG ) or magnetoencephalography ( MEG ) signals from the brain . 🔊
Meta近期开源了Brain2Qwerty v2，这是一种非侵入式脑机接口（BCI），能够利用大脑的脑电图（EEG）或脑磁图（MEG）信号，从思维中解码出句子。
In evaluations , the system achieved a word accuracy rate 61% on average , compared to 8% for other non-invasive methods . 🔊
在评估中，该系统平均单词准确率达到61%，而其他非侵入方法仅为8%。
Brain2Qwerty uses a three-stage deep-learning model to predict characters from the brain signals . 🔊
Brain2Qwerty采用三阶段深度学习模型，从脑信号中预测字符。
During data collection , participants were shown sentences and asked to remember them before typing . 🔊
在数据收集过程中，参与者会看到一些句子，并被要求先记住这些句子再打字。
Meta found that the MEG signals performed better , with a character error rate ( CER ) average of 29% vs . EEG's 65%. 🔊
Meta发现MEG信号的表现更优，其平均字符错误率（CER）为29%，而EEG为65%。
Compared to the baseline EEGNet model , Brain2Qwerty had a 2.5x better CER . 🔊
与基线EEGNet模型相比，Brain2Qwerty的字符错误率提升了2.5倍。
To help research on open models of the brain , Meta made both the model code and the training data available online . 🔊
为了推动大脑开放模型的研究，Meta已将模型代码和训练数据在线公开。
According to Meta , We believe this research has the potential to make a real difference for the millions of people who suffer from brain lesions that prevent them from communicating ... 🔊
据Meta称，我们相信这项研究有潜力为数百万因脑损伤而无法沟通的人们带来真正的改变……
We also find that decoding accuracy improves log-linearly with data volume , suggesting that the remaining performance gap with surgical approaches could be further narrowed through data scaling alone ... 🔊
我们还发现，解码精度随数据量呈对数线性提升，这表明仅通过扩大数据规模就能进一步缩小与手术方法之间的性能差距……
We do this in close collaboration with the community , through our recent $5 million fund to stimulate open datasets in our Digital Brain Project . 🔊
我们通过与社区的紧密合作，利用近期设立的500万美元基金来推动“数字大脑项目”中的开放数据集建设。
Our hope is that this work , done in the open , advances neuroscience to identify , diagnose , and treat neurological disorders faster than in siloes . 🔊
我们希望这项公开的工作能比封闭环境更快地推动神经科学在识别、诊断和治疗神经系统疾病方面的进展。
Previous work in non-invasive techniques has been limited by the ' noise complexity ' in the brain signals . 🔊
以往的非侵入技术研究一直受到脑信号中“噪声复杂性”的限制。
Invasive techniques such as electrocorticography ( ECoG ) are more reliable , but because they require surgery they are ' difficult to scale ,' according to Meta . 🔊
侵入式技术如皮质电图（ECoG）更为可靠，但Meta表示，由于需要手术，这些技术“难以规模化”。
In 2025, Meta released Brain2Qwerty v1; the new model has a word error rate ( WER ) that is nearly twice as good , ' significantly narrowing the gap ' with the WER of invasive techniques . 🔊
2025年，Meta发布了Brain2Qwerty v1；新模型的词错误率（WER）几乎提升了一倍，“显著缩小了”与侵入技术WER之间的差距。
Brain2Qwerty v2 contains three modules : an Encoder that takes brain signals as input and outputs character predictions ; an Aligner that groups characters into words ; and an LLM that generates the final output from the aligned data . 🔊
Brain2Qwerty v2包含三个模块：编码器（接收脑信号并输出字符预测）、对齐器（将字符组合成单词）以及大语言模型（根据对齐数据生成最终输出）。
One unexpected result of this architecture is that the system can correct ' typographical ' errors when the human users misspell words . 🔊
该架构的一个意外结果是：系统能够在人类用户拼错单词时纠正“打字”错误。
In a post on X about the new release , io . net co-founder Tory Green compared the performance of Brain2Qwerty v2 to v1: Seems like the jump from v1 to v2 came almost entirely from 10x more training data , not an architectural breakthrough . 🔊
在X平台关于新版本的帖子中，io.net联合创始人Tory Green比较了Brain2Qwerty v2与v1的性能：从v1到v2的提升几乎完全来自10倍的训练数据，而非架构上的突破。
That's actually the more exciting result . 🔊
这实际上是更令人兴奋的结果。
It means the limiting factor right now is labeled data from people wearing MEG headsets , not the fundamental difficulty of the problem . 🔊
这意味着当前的限制因素是来自佩戴MEG头戴设备人员的标注数据，而非问题本身的根本难度。
Constraints like that have a way of getting solved faster than people expect . 🔊
诸如此类的限制条件往往能以超出人们预期的速度得到解决。
The Brain2Qwerty v2 code is available on Github and the training data can be downloaded from Huggingface . 🔊
Brain2Qwerty v2的代码已在GitHub上提供，训练数据可从Huggingface下载。
Brain2Qwerty is part of Meta's Digital Brain project , which '[ open-sources ] the modeling of brain activity for science and medicine .' 🔊
Brain2Qwerty是Meta“数字大脑”项目的一部分，该项目“为科学和医学开源了大脑活动建模”。
Other Digital Brain artifacts include NeuralSet , a Python package for processing neural signals such as MEG and EEG ; and NeuralBench , a ' unified framework for benchmarking AI models of brain activity .' 🔊
其他数字大脑成果包括NeuralSet（用于处理MEG、EEG等神经信号的Python包）和NeuralBench（一个“用于基准测试大脑活动AI模型的统一框架”）。