2023,
Nature.
DOI:
10.1038/s41586-023-06377-x
A high-performance speech neuroprosthesis
Francis R. Willett, Erin M. Kunz, Chaofei Fan, Donald T. Avansino, Guy H. Wilson, Eun Young Choi, Foram Kamdar, Matthew F. Glasser, Leigh R. Hochberg, Shaul Druckmann, Krishna V. Shenoy, Jaimie M. Henderson
Abstract:
Abstract Speech brain–computer interfaces (BCIs) have the potential to restore rapid communication to people with paralysis by decoding neural activity evoked by attempted speech into text1,2 or sound3,4 . Early demonstrations, although promising, have not yet achieved accuracies sufficiently high for communication of unconstrained sentences from a large vocabulary1–7 . Here we demonstrate a speech-to-text BCI that records spiking activity from intracortical microelectrode arrays. Enabled by these high-resolution recordings, our study participant—who can no longer speak intelligibly owing to amyotrophic lateral sclerosis—achieved a 9.1% word error rate on a 50-word vocabulary (2.7 times fewer errors than the previous state-of-the-art speech BCI2 ) and a 23.8% word error rate on a 125,000-word vocabulary (the first successful demonstration, to our knowledge, of large-vocabulary decoding). Our participant’s attempted speech was decoded at 62 words per minute, which is 3.4 times as fast as the previous record8 and begins to approach the speed of natural conversation (160 words per minute9 ). Finally, we highlight two aspects of the neural code for speech that are encouraging for speech BCIs: spatially intermixed tuning to speech articulators that makes accurate decoding possible from only a small region of cortex, and a detailed articulatory representation of phonemes that persists years after paralysis. These results show a feasible path forward for restoring rapid communication to people with paralysis who can no longer speak.
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2024-01-31 23:23:00
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#paper doi：doi.org/10.1038/s41586-023-06377-x A high-performance speech neuroprosthesis
本文介绍了脑机接口在将脑神经信号转化为文本语言的尝试。文章在患有延髓性肌萎缩侧索硬化症 (ALS)患者的大脑6v （entral premotor cortex）区域和44 (布洛卡区)使用四个微电极阵列检测神经活动信号，训练了一个循环神经网络 (RNN) 解码器，以在每 80 毫秒的时间步长预测当时说出每个音素的概率，将这些概率与语言模型相结合，神经活动信号以每分钟 62 个单词的速度被解码。在 50 个单词的数据集中实现了 9.1% 的单词错误率，125000 个单词的数据集的单词错误率为 23.8%。
同时布洛卡区作用在语言产生的高阶方面，但它似乎几乎不包含音素或单词的信息，即使在瘫痪多年后，患者仍存在音素发音的细节，说明仅从 6v 小区域检测的神经活动信号开发出以正常会话速度恢复瘫痪患者通信设备的可行性。