A Wireless micro-ECoG Prosthesis for Speech

用于语音的无线微型 ECoG 假肢

• 批准号: 10706320
• 负责人: Kenneth L Shepard
• 金额: $ 61.84万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-17 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10706320
• 关键词: Algorithms; Amyotrophic Lateral Sclerosis; Area; Auditory; Auditory area; Brain; Clinical; Communication; Custom; Data; Development; Electrocorticogram; Electrodes; Electroencephalography; Environment; Eye Movements; Family; Friends; Generations; Health Personnel; Human; Implant; Infection prevention; Locked-In Syndrome; Machine Learning; Maps; Measures; Modeling; Motor Cortex; Muscular Dystrophies; Myopathy; Neuromuscular Diseases; Patients; Performance; Prosthesis; Quality of life; Reading; Research; Resolution; Sampling; Self-Help Devices; Signal Transduction; Speech; Techniques; Technology; Testing; Time; Training; Translating; Translations; Voice; Wireless Technology; Work; battery life; brain surgery; density; design; high dimensionality; improved; infection risk; integrated circuit; liquid crystal polymer; machine learning algorithm; meter; motor disorder; neural; neural prosthesis; neurotransmission; novel; power consumption; reading ability; reconstruction; signal processing; success; wireless

Project Summary / Abstract Patients who suffer from debilitating neuromuscular disorders (e.g. amyotrophic lateral sclerosis – ALS, Locked- in-Syndrome – LIS, and muscular dystrophies/myopathies) have difficulty or an inability to communicate through speech leading to a detrimental loss in quality of life. Current technology using eye movements and signals/spellers from electroencephalography (EEG) are slow and inconsistent. Neural prostheses offer an opportunity to produce fast and accurate communication for patients suffering from neuromuscular disorders, but success for regaining speech has been limited due to technological limitations: there is an inability to capture the high dimensionality of the brain and an inability to record in naturalistic conditions using fully implanted, wireless electrode arrays. To solve these challenges, we develop and optimize custom wireless micro- electrocorticographic (µECoG) arrays with over 1,000 channels to decode speech directly from the human brain. We will accomplish this by 1) Testing and optimizing the spatial resolution of µECoG to capture neural signals, 2) Fine-tune our machine learning algorithms to decode speech directly from the brain and 3) developing wireless technology to enable neural prosthetic usage in naturalistic settings. High-density, high channel-count neural interfaces will offer an unprecedented ability to decode speech from the human brain. This ability combined with wireless technology, will allow for a new generation of speech neural prostheses.

项目概要/摘要 患有使人衰弱的神经肌肉疾病的患者（例如肌萎缩侧索硬化症 – ALS、锁定 – 综合症 – LIS 和肌营养不良症/肌病）有困难或无法通过 言语会导致生活质量下降。当前的技术使用眼球运动和 脑电图 (EEG) 的信号/拼写器缓慢且不一致。神经假体提供了 为患有神经肌肉疾病的患者提供快速准确的沟通的机会， 但由于技术限制，恢复言语的成功受到限制：无法捕获 大脑的高维性和无法在自然条件下使用完全植入进行记录， 无线电极阵列。为了解决这些挑战，我们开发和优化定制无线微型 皮层电图 (μECoG) 阵列具有 1,000 多个通道，可直接解码来自人脑的语音。 我们将通过以下方式实现这一目标：1) 测试和优化 µECoG 的空间分辨率以捕获神经信号， 2) 微调我们的机器学习算法，直接从大脑解码语音；3) 开发无线技术 能够在自然环境中使用神经假体的技术。高密度、高通道数神经网络 接口将提供前所未有的解码人脑语音的能力。这种能力结合 无线技术将允许新一代语音神经假体的出现。