Srinivasan Aparna, Wairagkar Maitreyee, Iacobacci Carrina, Hou Xianda, Card Nicholas S, Jacques Brandon G, Pritchard Anna L, Bechefsky Payton H, Hochberg Leigh R, AuYong Nicholas, Pandarinath Chethan, Brandman David M, Stavisky Sergey D
Department of Neurological Surgery, University of California Davis, Davis, CA, USA.
Biomedical Engineering Graduate Group, University of California Davis, Davis, CA, USA.
bioRxiv. 2025 May 31:2025.05.30.657105. doi: 10.1101/2025.05.30.657105.
The ability to vary the mode and loudness of speech is an important part of the expressive range of human vocal communication. However, the encoding of these behaviors in the ventral precentral gyrus (vPCG) has not been studied at the resolution of neuronal firing rates. We investigated this in two participants who had intracortical microelectrode arrays implanted in their vPCG as part of a speech neuroprosthesis clinical trial. Neuronal firing rates modulated strongly in vPCG as a function of attempted mimed, whispered, normal or loud speech. At the neural ensemble level, mode/loudness and phonemic content were encoded in distinct neural subspaces. Attempted mode/loudness could be decoded from vPCG with an accuracy of 94% and 89% for two participants respectively, and corresponding neural preparatory activity could be detected hundreds of milliseconds before speech onset. We then developed a closed-loop loudness decoder that achieved 94% online accuracy in modulating a brain-to-text speech neuroprosthesis output based on attempted loudness. These findings demonstrate the feasibility of decoding mode and loudness from vPCG, paving the way for speech neuroprostheses capable of synthesizing more expressive speech.
改变语音模式和响度的能力是人类语音交流表达范围的重要组成部分。然而,腹侧中央前回(vPCG)中这些行为的编码尚未在神经元放电率的分辨率水平上进行研究。我们在两名参与者中对此进行了调查,这两名参与者作为语音神经假体临床试验的一部分,在其vPCG中植入了皮质内微电极阵列。随着尝试模仿、低语、正常或大声说话,vPCG中的神经元放电率会强烈调制。在神经集合水平上,模式/响度和音素内容被编码在不同的神经子空间中。两名参与者尝试的模式/响度分别可以从vPCG中以94%和89%的准确率解码,并且相应的神经准备活动可以在语音开始前数百毫秒被检测到。然后,我们开发了一种闭环响度解码器,在基于尝试的响度调制脑到文本语音神经假体输出方面实现了94%的在线准确率。这些发现证明了从vPCG解码模式和响度的可行性,为能够合成更具表现力语音的语音神经假体铺平了道路。