This paper briefly reviews current silent speech methodologies for normal and disabled individuals. Current techniques utilizing electromyographic (EMG) recordings of vocal tract movements are useful for physically healthy individuals but fail for tetraplegic individuals who do not have accurate voluntary control over the speech articulators. Alternative methods utilizing EMG from other body parts (e.g., hand, arm, or facial muscles) or electroencephalography (EEG) can provide capable silent communication to severely paralyzed users, though current interfaces are extremely slow relative to normal conversation rates and require constant attention to a computer screen that provides visual feedback and/or cueing. We present a novel approach to the problem of silent speech via an intracortical microelectrode brain–computer interface (BCI) to predict intended speech information directly from the activity of neurons involved in speech production. The predicted speech is synthesized and acoustically fed back to the user with a delay under 50 ms. We demonstrate that the Neurotrophic Electrode used in the BCI is capable of providing useful neural recordings for over 4 years, a necessary property for BCIs that need to remain viable over the lifespan of the user. Other design considerations include neural decoding techniques based on previous research involving BCIs for computer cursor or robotic arm control via prediction of intended movement kinematics from motor cortical signals in monkeys and humans. Initial results from a study of continuous speech production with instantaneous acoustic feedback show the BCI user was able to improve his control over an artificial speech synthesizer both within and across recording sessions. The success of this initial trial validates the potential of the intracortical microelectrode-based approach for providing a speech prosthesis that can allow much more rapid communication rates.

本文简要回顾了当前针对正常人和残疾人的无声语音方法。当前利用声带运动的肌电图（EMG）记录的技术对于身体健康的人很有用，但对没有对语音发音器进行准确的自愿控制的四肢瘫痪的人则无效。利用其他身体部位（例如手，手臂或面部肌肉）的EMG或脑电图（EEG）的替代方法可以为严重瘫痪的用户提供有能力的沉默交流，尽管当前界面相对于正常会话率而言非常慢，并且需要不断关注提供视觉反馈和/或提示的计算机屏幕。我们提出了一种通过皮质内微电极脑计算机接口（BCI）来解决无声语音问题的新颖方法，以直接从涉及语音产生的神经元活动预测预期的语音信息。合成预测的语音，并以不到50毫秒的延迟将声音回馈给用户。我们证明了BCI中使用的神经营养性电极能够提供4年以上的有用神经记录，这对于BCI来说是必不可少的属性，而BCI在用户的整个生命周期中都必须保持活力。其他设计考虑因素包括基于先前研究的神经解码技术，该技术涉及BCI，用于通过预测猴子和人类运动皮层信号的预期运动运动学来控制计算机光标或机械臂。一项对具有瞬时声反馈的连续语音产生进行研究的初步结果表明，BCI用户能够改善在录制期间内和录制期间对人工语音合成器的控制。这项初步试验的成功验证了基于皮层内微电极的方法提供语音假体的潜力，该假体可以允许更快的通讯速率。