Martin Stephanie, Iturrate Iñaki, Millán José Del R, Knight Robert T, Pasley Brian N
Defitech Chair in Brain Machine Interface, Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States.
Front Neurosci. 2018 Jun 21;12:422. doi: 10.3389/fnins.2018.00422. eCollection 2018.
Certain brain disorders resulting from brainstem infarcts, traumatic brain injury, cerebral palsy, stroke, and amyotrophic lateral sclerosis, limit verbal communication despite the patient being fully aware. People that cannot communicate due to neurological disorders would benefit from a system that can infer internal speech directly from brain signals. In this review article, we describe the state of the art in decoding inner speech, ranging from early acoustic sound features, to higher order speech units. We focused on intracranial recordings, as this technique allows monitoring brain activity with high spatial, temporal, and spectral resolution, and therefore is a good candidate to investigate inner speech. Despite intense efforts, investigating how the human cortex encodes inner speech remains an elusive challenge, due to the lack of behavioral and observable measures. We emphasize various challenges commonly encountered when investigating inner speech decoding, and propose potential solutions in order to get closer to a natural speech assistive device.
某些由脑干梗死、创伤性脑损伤、脑瘫、中风和肌萎缩侧索硬化症导致的脑部疾病，尽管患者意识完全清醒，但仍会限制言语交流。因神经障碍而无法交流的人将受益于一种能够直接从脑信号推断内部言语的系统。在这篇综述文章中，我们描述了从早期声学特征到高阶言语单元的内部言语解码的最新技术水平。我们专注于颅内记录，因为这项技术能够以高空间、时间和频谱分辨率监测大脑活动，因此是研究内部言语的理想选择。尽管付出了巨大努力，但由于缺乏行为和可观察的测量方法，研究人类皮层如何编码内部言语仍然是一个难以捉摸的挑战。我们强调了在研究内部言语解码时常见的各种挑战，并提出了潜在的解决方案，以便更接近自然的言语辅助设备。
