Wearable Magnetoencephalography (MEG) to detect brain activity.

我的研究项目旨在开发一种可穿戴（头盔或帽子）脑磁图（MEG）系统。MEG系统是一种医疗设备，用于通过磁传感器检测大脑的活动。神经生理过程产生电场和磁场。在更常见的脑电图（EEG）系统使用电探针来检测电神经元脉冲的情况下，由于通过颅骨和头皮的电场的衰减，它不能提供大脑活动的精确空间分辨率。利用磁传感器已经证明MEG可以提供更明确的信息。该项目旨在通过使其可穿戴并更易于在临床环境中使用来使用磁性传感器。现有的MEG系统体积庞大，具有特殊要求，例如金属绝缘室以防止背景辐射。MEG的磁传感器也必须在-270摄氏度左右的极冷温度下工作，这导致了一个巨大而昂贵的系统，无法在小型全科医生诊所（GP）中使用。帽中的隧道磁阻（TMR）传感器将检测大脑神经元产生的磁场并将其转换为信号。该信号将是嘈杂的（不需要的信号的高干扰），并且不能用于任何应用。噪声将来自邻近的神经元、背景辐射和布线。因此，该项目的下一步是消除机器/系统中的噪声，以提供更清晰，更平滑的信号。这个过程被称为信号处理，任何信号都可以在真实的时间内被过滤，以提供所需的信号。然而，并非所有的噪音都可以被去除，通过使用机器学习算法，将学习从噪音中分离神经活动，将能够区分纯大脑信号。该项目的下一步需要不断测试算法，因为算法的准确性和处理速度取决于进行的测试数量。一旦获得了纯净的信号，神经学家将能够通过计算机看到实际的大脑活动，就像目前的MEG机器一样。这项拟议中的研究还将有其他潜在的应用。例如，科学家已经测试了一个喂食机器人，通过识别负责运动功能的大脑区域，将探针嵌入猴子的大脑。这只猴子能够通过脑机接口系统控制机器人手臂喂它香蕉。因此，如果能够开发出可穿戴的MEG帽，人类将能够用大脑控制机器人甚至假肢，其精度比目前使用基于MEG的设备要好得多。建立一个精确的，无噪声和便携式系统记录神经元活动是最重要的先决条件，以广泛接受脑计算机接口系统在临床或家庭环境。

My research project aims to develop a wearable (helmet or cap) Magnetoencephalography (MEG) system. A MEG system is a medical device used to detect the brain's activity with magnetic sensors. Neurophysiological process creates both electrical and magnetic field. Where the more common, electroencephalography (EEG) system, uses electrical probes to detect electrical neuron pulses, it fails to provide a precise spatial resolution of the brains activities, due to attenuation of electrical filed through scull and scalp. MEG has already been proved to give much more explicit information using magnetic sensors. The project aims to use miniaturised magnetic sensors by making it wearable and more accessible to use in clinical environments. The existing MEG systems are bulky with special requirements, such as metal insulated rooms to prevent background radiation. The magnetic sensors of a MEG must also be extremely cold around -270 degrees Celsius and result in a huge and expensive system, which cannot be used in a small general practitioner's clinic (GP).The tunnel magnetoresistance (TMR) sensor in the cap will detect the generated magnetic field from the brain neurons and convert it into a signal. This signal will be noisy (high interference of unwanted signal) and cannot be used for any application. The noise will come from adjacent neurons, background radiation and wirings. Therefore, the next step in this project is to remove noise from the machine/system to give a clearer and smoother signal. This process is called signal processing, where any signal can be filtered in real time to provide the desired signal.However, not all noise can be removed and by using a machine-learning algorithm that will learn to separate neural activity from noise, will be able to distinguish a pure brain signal. This next step of the project requires the algorithm to be continually tested as the accuracy and process speed of the algorithm is depended on the number of tests conducted. Once a pure signal is obtained, a neurologist will be able to see the actual brain activity by computers, just like the current MEG machines.The proposed research will also have other potential applications. Such as scientists have already tested a feeding robot, whereby identifying the region of the brain responsible for motor functions, a probe was embedded in a monkey's brain. The monkey was able to control a robotic arm to feed it bananas via the brain-computer interface system. Therefore, if a wearable MEG cap can be developed, humans will be able to control robots or even prosthetics with their minds with much better precision than it is currently available using MEG-based devices. Creating a precise, noise free and portable systems for recording neuronal activity is the most important prerequisite towards wide spread acceptance of brain computer interface systems in clinical or home environments.