A Wireless micro-ECoG Prosthesis for Speech

用于语音的无线微型 ECoG 假肢

基本信息

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
项目类别：
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

项目摘要

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导致生活质量的痛苦下降。脑电图 (EEG) 的信号/拼写器缓慢且不一致。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，为了解决这些挑战，我们开发并优化了定制无线微型电极阵列。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) 开发无线技术技术可在自然环境中使用神经假体。接口将提供前所未有的解码人脑语音的能力。无线技术将允许新一代语音神经假体的出现。