Brain–computer interfaces (BCIs) can restore communication to people who have lost the ability to move or speak. So far, a major focus of BCI research has been on restoring gross motor skills, such as reaching and grasping^1,2,3,4,5 or point-and-click typing with a computer cursor^6,7. However, rapid sequences of highly dexterous behaviours, such as handwriting or touch typing, might enable faster rates of communication. Here we developed an intracortical BCI that decodes attempted handwriting movements from neural activity in the motor cortex and translates it to text in real time, using a recurrent neural network decoding approach. With this BCI, our study participant, whose hand was paralysed from spinal cord injury, achieved typing speeds of 90 characters per minute with 94.1% raw accuracy online, and greater than 99% accuracy offline with a general-purpose autocorrect. To our knowledge, these typing speeds exceed those reported for any other BCI, and are comparable to typical smartphone typing speeds of individuals in the age group of our participant (115 characters per minute)⁸. Finally, theoretical considerations explain why temporally complex movements, such as handwriting, may be fundamentally easier to decode than point-to-point movements. Our results open a new approach for BCIs and demonstrate the feasibility of accurately decoding rapid, dexterous movements years after paralysis.

脑机接口 (BCI) 可以恢复与失去移动或说话能力的人的交流。到目前为止，BCI 研究的一个主要重点是恢复粗大运动技能，例如达到和掌握^{1、2、3、4、5}或使用计算机光标进行点击式打字^6,7. 然而，高度灵巧的行为（例如手写或触摸打字）的快速序列可能会加快沟通速度。在这里，我们开发了一种皮质内 BCI，它使用循环神经网络解码方法从运动皮层的神经活动中解码尝试的手写动作，并将其实时翻译成文本。有了这个 BCI，我们的研究参与者（他的手因脊髓损伤而瘫痪）实现了每分钟 90 个字符的打字速度，在线原始准确率为 94.1%，而通用自动更正的离线准确率超过 99%。据我们所知，这些打字速度超过了任何其他 BCI 报告的速度，并且与我们参与者年龄组中个人的典型智能手机打字速度（每分钟 115 个字符）相当⁸. 最后，理论考虑解释了为什么时间上复杂的运动，例如手写，可能比点对点运动更容易解码。我们的结果为 BCI 开辟了一种新方法，并证明了在瘫痪数年后准确解码快速、灵巧运动的可行性。