Wairagkar Maitreyee, Card Nicholas S, Singer-Clark Tyler, Hou Xianda, Iacobacci Carrina, Miller Lee M, Hochberg Leigh R, Brandman David M, Stavisky Sergey D
Department of Neurological Surgery, University of California, Davis, Davis, CA, USA.
Department of Biomedical Engineering, University of California, Davis, Davis, CA, USA.
Nature. 2025 Jun 12. doi: 10.1038/s41586-025-09127-3.
Brain-computer interfaces (BCIs) have the potential to restore communication for people who have lost the ability to speak owing to a neurological disease or injury. BCIs have been used to translate the neural correlates of attempted speech into text. However, text communication fails to capture the nuances of human speech, such as prosody and immediately hearing one's own voice. Here we demonstrate a brain-to-voice neuroprosthesis that instantaneously synthesizes voice with closed-loop audio feedback by decoding neural activity from 256 microelectrodes implanted into the ventral precentral gyrus of a man with amyotrophic lateral sclerosis and severe dysarthria. We overcame the challenge of lacking ground-truth speech for training the neural decoder and were able to accurately synthesize his voice. Along with phonemic content, we were also able to decode paralinguistic features from intracortical activity, enabling the participant to modulate his BCI-synthesized voice in real time to change intonation and sing short melodies. These results demonstrate the feasibility of enabling people with paralysis to speak intelligibly and expressively through a BCI.
脑机接口（BCIs）有潜力为因神经疾病或损伤而失去说话能力的人恢复沟通能力。脑机接口已被用于将尝试说话的神经关联转化为文本。然而，文本交流无法捕捉人类语音的细微差别，如韵律和即时听到自己的声音。在此，我们展示了一种脑到语音神经假体，通过解码植入一名患有肌萎缩侧索硬化症和严重构音障碍男子腹侧中央前回的256个微电极的神经活动，利用闭环音频反馈即时合成语音。我们克服了缺乏用于训练神经解码器的真实语音这一挑战，并能够准确合成他的声音。除了音素内容，我们还能够从皮层内活动中解码副语言特征，使参与者能够实时调节其脑机接口合成的语音，以改变语调并演唱简短旋律。这些结果证明了使瘫痪患者通过脑机接口清晰且富有表现力地说话的可行性。
