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Empowering Next Generation Implantable Neural Interfaces

赋能下一代植入式神经接口

基本信息

批准号：
EP/M020975/1
负责人：
Timothy Constandinou
金额：
$ 129.53万
依托单位：
Imperial College London
依托单位国家：
英国
项目类别：
Fellowship
财政年份：
2015
资助国家：
英国
起止时间：
2015 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FM020975%2F1
关键词：
Empowering Next Generation Implantable Neural

项目摘要

Being able to control devices with our thoughts is a concept that has for long captured the imagination. Neural Interfaces or Brain Machine Interfaces (BMIs) are devices that aim to do precisely this. Next generation devices will be distributed like the brain itself. It is currently estimated that if we were able to record electrical activity simultaneously from between 1,000 and 10,000 neurons, this would enable useful prosthetic control (e.g. of a prosthetic arm). However, rather than relying on a single, highly complex implant and trying to cram more and more channels in this (the current paradigm), the idea here is to develop a simpler, smaller, well-engineered primitive and deploy multiple such devices. It is essential these are each compact, autonomous, calibration-free, and completely wireless. It is envisaged that each device will be mm-scale, and be capable of recording only a few channels (i.e. up to 20), but also perform real-time signal processing. This processing will achieve data reduction so as to wirelessly communicate only useful information, rather than raw data, which can most often be just noise and of no use. Making these underlying devices "simpler" will overcome many of the common challenges that are associated with scaling of neural interfaces, for example, wires breaking, biocompatibility of the packaging, thermal dissipation and yield. By distributing tens to hundreds of these in a "grid" of neural interfaces, many of the desirable features of distributed networks come into play; for example, redundancy and robustness to single component failure. A first tangible application for this platform will see these devices embedded in a uniform array within a flexible substrate for electrocorticography (i.e. recording from the surface of the brain). It will however, also be investigated how the underlying devices can be made applicable to other formats, for instance, in penetrating intracortical devices (recording from within the cortex). Such devices will communicate the neural "control signals" to an external prosthetic device. These can then, for example, be used for: an amputee to control a robotic prosthetic; a paraplegic to control a mobility aid; or an individual with locked in syndrome to communicate with the outside world.This Fellowship will consolidate expertise and build a core capability that can deliver such devices. This will be achieved by working together with researchers and professionals across multiple disciplines including ICT, engineering, healthcare technologies, medical devices and neuroscience. The research is extremely well aligned with the current quest to understand the brain; for example, US presidential BRAIN initiative, and the EU human brain project. It will impact neuroscience research, by extending current capabilities by at least an order of magnitude, but also medical devices by inventing and demonstrating a radically new approach.

能够用我们的思想控制设备是一个长期以来激发人们想象力的概念。神经接口或脑机接口(BMI)正是旨在做到这一点的设备。下一代设备将像大脑本身一样分布。目前估计，如果我们能够同时记录1,000到10,000个神经元的电活动，这将使有用的假肢控制(例如假臂)成为可能。然而，这里的想法是开发一种更简单、更小、设计良好的原件，并部署多个这样的设备，而不是依赖于单个高度复杂的植入物并试图在这种(当前的范例)中塞进越来越多的通道。它们都是紧凑的、自主的、免校准的和完全无线的，这一点至关重要。设想每个设备将是毫米级的，并且能够仅记录几个频道(即最多20个)，但也执行实时信号处理。这种处理将实现数据缩减，以便无线地仅传送有用的信息，而不是原始数据，后者通常只是噪声和无用的。使这些底层设备“更简单”将克服许多与神经接口缩放相关的常见挑战，例如电线断裂、包装的生物兼容性、散热和成品率。通过在神经接口的“网格”中分布数十到数百个这样的网络，分布式网络的许多理想特征都发挥了作用；例如，冗余和对单个组件故障的稳健性。该平台的第一个实际应用将看到这些设备嵌入柔性衬底中的统一阵列，用于脑电成像(即从大脑表面记录)。然而，还将研究如何使底层设备适用于其他格式，例如，穿透皮质内设备(从皮质内记录)。这种装置会将神经“控制信号”传递给外部假肢装置。例如，这些设备可以用于：截肢者控制机器人假肢；截瘫患者控制行动辅助设备；或患有闭锁综合症的人与外部世界沟通。这一奖学金将巩固专业知识，并建立能够提供此类设备的核心能力。这将通过与包括ICT、工程学、医疗保健技术、医疗器械和神经科学在内的多个学科的研究人员和专业人员合作来实现。这项研究与当前对大脑的探索非常一致；例如，美国总统的大脑倡议和欧盟的人类大脑项目。它将影响神经科学研究，将目前的能力至少扩展一个数量级，但也将通过发明和展示一种全新的方法来影响医疗设备。