Optical Tomography and Decoding for Communication via Brain-Computer Interface

通过脑机接口进行通信的光学断层扫描和解码

• 批准号: 9911583
• 负责人: Zachary E Markow
• 金额: $ 4.5万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9911583
• 关键词: Adult; Amyotrophic Lateral Sclerosis; Augmentative and Alternative Communication; Biomedical Engineering; Blood; Brain; Brain imaging; Brain scan; Cerebral Palsy; Child; Clip; Collection; Color; Communication; Computers; Data; Devices; Electrocorticogram; Electrodes; Electroencephalography; Elements; Equipment; Eye; Fellowship; Foundations; Frequencies; Functional Magnetic Resonance Imaging; Goals; Hearing; Image; Imagination; Instruction; Language; Learning; Letters; Light; Limb Prosthesis; Literature; Locked-In Syndrome; Machine Learning; Maps; Methods; Modeling; Motor; Muscle; Near-Infrared Spectroscopy; Operative Surgical Procedures; Optical Tomography; Optics; Patients; Pattern; Performance; Phase; Physiological; Property; Psyche structure; Quality of life; Research; Research Project Grants; Resolution; Semantics; Shapes; Signal Transduction; Speech; Stimulus; Surface; System; Technology; Temporal Lobe; Testing; Training; Travel; Visual; Visual Cortex; Work; base; brain computer interface; career; cost; density; diffuse optical tomography; experience; experimental study; extrastriate visual cortex; frontal lobe; human subject; imaging approach; imaging system; improved; light weight; mental imagery; motor impairment; movie; neuroimaging; non-invasive system; optical imaging; portability; response; success; tool; virtual

Project Summary: The long-term goal of this research is to develop a new, non-invasive brain-computer interface (BCI) that will provide augmentative and alternative communication (AAC) capabilities to patients who have lost these capabilities due to severe motor impairments, such as completely locked-in syndrome (CLIS), amyotrophic lateral sclerosis (ALS), and severe cerebral palsy (CP). The proposed research project will work towards a long-term goal by developing a BCI based on brain imaging signals from high-density diffuse optical tomography (HDDOT). Some existing BCIs record electroencephalography (EEG) or electrocorticography (ECoG) signals from patients and then decode these signals into instructions for operating some element of the outside world, such as a cursor on a screen, a prosthetic limb, or a virtual keyboard. This functionality can enable communication. However, BCI generally has had limited success and capabilities in CLIS patients with EEG or has relied on invasive technology such as ECoG or intracortical recordings, which require surgical placement of electrodes on or beneath the brain surface. Although functional MRI (fMRI) has recently achieved great success with decoding items viewed or heard by subjects (e.g., distinguishing from among >100 viewed images), fMRI requires bulky, expensive equipment that cannot be employed for routine BCI for patients with severe motor- related communication deficits. In contrast, optical imaging approaches, such as near-infrared spectroscopy (NIRS), employ portable, wearable hardware. These optical systems are non-invasive and use non-ionizing, near-infrared light to create movies of blood oxygenation and therefore provide physiological information comparable to the fMRI signal. NIRS has recently been applied as an alternative to EEG BCI for decoding simple yes/no responses in CLIS patients. However, NIRS systems suffer from much-lower spatial resolution than fMRI, which renders NIRS unlikely to match the decoding capabilities of fMRI. High-density diffuse optical tomography (HDDOT) combines the lightweight, low-cost equipment benefits of EEG and NIRS with higher spatial resolution closer to that of fMRI at the brain surface. Recent advances in HDDOT systems have enabled average spatial localization errors <5 mm and spatial resolution <17-20 mm (substantially better than NIRS). Studies have demonstrated detailed maps of both visual and language tasks. These properties make HDDOT an ideal candidate tool for decoding brain function. The fellowship training will provide a strong foundation in optical neuroimaging methods, machine learning, and brain-computer interface. These experiences will prepare the applicant exceptionally well for a career in biomedical engineering research and for developing technology that will improve these patients’ quality of life.

项目概要：本研究的长期目标是开发一种新型的、非侵入性的脑机 接口（BCI）将为患者提供增强和替代通信（AAC）功能 由于严重的运动障碍，例如完全锁定综合症（CLIS）而失去了这些能力， 肌萎缩侧索硬化症（ALS）和严重脑瘫（CP）。拟议的研究项目将发挥作用 通过开发基于高密度漫射光学脑成像信号的脑机接口来实现长期目标 断层扫描（HDDOT）。一些现有的脑机接口记录脑电图 (EEG) 或皮层电图 （ECoG）来自患者的信号，然后将这些信号解码为操作某些元件的指令 外部世界，例如屏幕上的光标、假肢或虚拟键盘。此功能可以启用 沟通。然而，BCI 在接受脑电图或脑电图检查的 CLIS 患者中通常取得的成功和能力有限。 依赖于侵入性技术，例如 ECoG 或皮质内记录，这需要手术放置 大脑表面或下方的电极。尽管功能性磁共振成像（fMRI）最近取得了巨大成功 解码受试者看到或听到的项目（例如，从 >100 个观看的图像中区分）、fMRI 需要笨重、昂贵的设备，无法用于患有严重运动障碍的患者的常规 BCI 相关的沟通缺陷。相比之下，光学成像方法，例如近红外光谱 (NIRS)，采用便携式、可穿戴硬件。这些光学系统是非侵入性的并且使用非电离、 近红外光创建血液氧合的电影，从而提供生理信息 与功能磁共振成像信号相当。 NIRS 最近已被用作脑电图 BCI 的替代方案，用于简单解码 CLIS 患者的是/否反应。然而，NIRS 系统的空间分辨率比 fMRI 低得多， 这使得 NIRS 不太可能与功能磁共振成像的解码能力相匹配。高密度漫射光学断层扫描 (HDDOT) 将 EEG 和 NIRS 的轻量级、低成本设备优势与更高的空间分辨率结合在一起 更接近大脑表面的功能磁共振成像。 HDDOT 系统的最新进展使平均空间 定位误差 <5 毫米，空间分辨率 <17-20 毫米（明显优于 NIRS）。研究有 展示了视觉和语言任务的详细地图。这些特性使 HDDOT 成为理想的 解码大脑功能的候选工具。奖学金培训将为光学领域奠定坚实的基础 神经影像方法、机器学习和脑机接口。这些经验将为 申请人在生物医学工程研究和开发技术方面表现出色 将改善这些患者的生活质量。