Brain2Qwerty v2 達到 39% 詞錯率 —— 非侵入式 BCI 能否最終戰勝手術？
meta 的 fair 實驗室發布了 brain2qwerty v2，這是一個用於從 meg 腦訊號中解碼語音的模型，詞錯率達到 39%。這使得非侵入式腦機介面在無需高風險手術的情況下更接近臨床應用。
meta released brain2qwerty v2, a model that achieves a 39% word error rate (wer) by decoding continuous brain signals from magnetoencephalography (meg) data without requiring invasive surgery. the system, trained on 10 times more data than its predecessor, uses a deep learning pipeline and a fine-tuned qwen3 language model to convert motor cortex activity into coherent sentences, achieving perfect decoding in 28% of attempts for the best participant. while the model significantly outperforms previous versions in semantic accuracy, its reliance on a language model causes it to hallucinate grammatically correct but factually incorrect sentences when signal data is weak, resulting in a higher character error rate than raw encoders. this research signals a shift toward using scalable software and larger datasets to solve the 'last mile' problem in non-invasive bci, potentially removing the need for high-risk surgical implants for speech restoration.