Department of Electrical Engineering, The Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA.
Department of Biomedical Engineering, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Neurotherapeutics. 2019 Jan;16(1):144-165. doi: 10.1007/s13311-018-00692-2.
A brain-computer interface (BCI) is a technology that uses neural features to restore or augment the capabilities of its user. A BCI for speech would enable communication in real time via neural correlates of attempted or imagined speech. Such a technology would potentially restore communication and improve quality of life for locked-in patients and other patients with severe communication disorders. There have been many recent developments in neural decoders, neural feature extraction, and brain recording modalities facilitating BCI for the control of prosthetics and in automatic speech recognition (ASR). Indeed, ASR and related fields have developed significantly over the past years, and many lend many insights into the requirements, goals, and strategies for speech BCI. Neural speech decoding is a comparatively new field but has shown much promise with recent studies demonstrating semantic, auditory, and articulatory decoding using electrocorticography (ECoG) and other neural recording modalities. Because the neural representations for speech and language are widely distributed over cortical regions spanning the frontal, parietal, and temporal lobes, the mesoscopic scale of population activity captured by ECoG surface electrode arrays may have distinct advantages for speech BCI, in contrast to the advantages of microelectrode arrays for upper-limb BCI. Nevertheless, there remain many challenges for the translation of speech BCIs to clinical populations. This review discusses and outlines the current state-of-the-art for speech BCI and explores what a speech BCI using chronic ECoG might entail.
脑机接口 (BCI) 是一种使用神经特征来恢复或增强其用户能力的技术。用于语音的 BCI 将通过尝试或想象语音的神经相关物来实现实时通信。这项技术有可能为闭锁症患者和其他严重语言障碍患者恢复沟通和提高生活质量。近年来，神经解码器、神经特征提取和脑记录模式方面取得了许多进展，这些进展有助于控制义肢的 BCI 和自动语音识别 (ASR)。事实上，ASR 及相关领域在过去几年中得到了显著发展，许多领域为语音 BCI 的要求、目标和策略提供了很多启示。神经语音解码是一个相对较新的领域，但最近的研究表明，使用脑电图 (ECoG) 和其他神经记录模式进行语义、听觉和发音解码，具有很大的潜力。由于语音和语言的神经表示广泛分布在跨越额叶、顶叶和颞叶的皮质区域，因此 ECoG 表面电极阵列捕获的群体活动的介观尺度可能对语音 BCI 具有明显的优势，与微电极阵列对上肢 BCI 的优势形成对比。然而，将语音 BCI 转化为临床人群仍然存在许多挑战。本文讨论并概述了语音 BCI 的最新现状，并探讨了使用慢性 ECoG 的语音 BCI 可能涉及的内容。
