Machine Learning and Signal Processing for Advances in Neurotechnology

机器学习和信号处理促进神经技术的进步

• 批准号: RGPIN-2016-06633
• 负责人: Reilly, James
• 金额: $ 2.26万
• 依托单位: McMaster University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=683950
• 关键词: Machine; Learning; Signal; Processing; Advances

For as long as human beings have been able to think, we have been trying to understand our own brains. However, as Emerson M. Pugh points out, ``If the brain were so simple we could understand it, we would be so simple we couldn't.'' Despite this inherent enigma, there has been intense, multi-disciplinary activity directed towards the understanding of the human brain in recent times. Neuroscientists, biochemists, psychologists and others have already been engaged for many decades on this topic, with impressive results. However, only recently have mathematicians and engineers realized that they too have an important role to play in furthering progress in this area.***Much of the activity in brain research may be encapsulated into the term neurotechnology. Neurotechnology has to do with the development of technologies that are designed to improve and repair brain function, or that facilitate the direct interaction between the human brain and a computer, i.e., brain-computer interfaces (BCIs). In this vein, this proposal deals with the application of mathematical and engineering principles in the development of new neurotech devices and procedures.****An enticing prospect we offer in the neurotech field is machine learning (ML). Since the brain is too complex to model directly, the machine learning approach in this context operates by constructing a rudimentary mathematical model using only observed data from the brain. The applicant's team has previously used ML analyses of the electroencephalogram (EEG) to diagnose mental illness, and also for BCI applications. Also the applicant's team was the first to develop novel ML methods for identifying effective treatments for major depression.***The mental/neuro health field is rife with opportunity for applications of ML. We intend to develop novel ML methods for predicting recovery from coma. We also intend to extend our newly developed ML algorithms to enhance treatments for mental illness. Thirdly, we intend to develop improved BCI devices using a novel interactive training approach where the human and the computer adapt to each other. We will also develop new ML tools to aid in accomplishing these goals. The first such tool is to extend the capabilities of our newly developed ML algorithms, and the second is to identify new, highly salient biomarkers from the EEG that represent networks in the brain.***The ML approach brings a fresh new engineering-centred perspective to solving the long-standing difficult neurotech problems we have proposed. Our machine learning approaches show significant promise for innovation in health care and in developing new BCI devices. Furthermore, our proposed ML tools are not only crucial to us achieving our goals, but will also offer the ML community new algorithms and techniques. Thus this research offers Canada the potential to assert its presence in the neurotech field by enhancing and improving the use of ML methods in brain research.***********

自从人类能够思考以来，我们就一直在试图了解我们自己的大脑。然而，正如爱默生·m·皮尤所指出的那样：“如果大脑简单到我们能够理解它，那么我们自己也会简单到我们无法理解它。”“尽管存在这个固有的谜团，但近年来，人们开展了激烈的多学科活动，旨在了解人类大脑。神经科学家、生化学家、心理学家和其他人已经对这个话题进行了几十年的研究，并取得了令人印象深刻的成果。然而，直到最近，数学家和工程师才意识到他们在这一领域的进一步发展中也扮演着重要的角色。大脑研究中的许多活动可以概括为“神经技术”这个术语。神经技术与旨在改善和修复大脑功能的技术发展有关，或者促进人类大脑和计算机之间的直接互动，即脑机接口（bci）。在这方面，本提案涉及数学和工程原理在开发新的神经技术设备和程序中的应用。****我们在神经技术领域提供的诱人前景是机器学习（ML）。由于大脑太复杂而无法直接建模，因此在这种情况下，机器学习方法通过仅使用来自大脑的观察数据构建一个基本的数学模型来运行。申请人的团队以前使用脑电图（EEG）的ML分析来诊断精神疾病，也用于脑机接口应用。此外，申请人的团队也是第一个开发新的ML方法来识别严重抑郁症的有效治疗方法的团队。***精神/神经健康领域充满了ML应用的机会。我们打算开发新的ML方法来预测昏迷的恢复。我们还打算扩展我们新开发的机器学习算法，以加强对精神疾病的治疗。第三，我们打算使用一种新的交互式训练方法开发改进的脑机接口设备，在这种方法中，人类和计算机相互适应。我们还将开发新的机器学习工具来帮助实现这些目标。第一个工具是扩展我们新开发的机器学习算法的功能，第二个工具是从脑电图中识别新的、高度突出的生物标记物，这些生物标记物代表了大脑中的网络。机器学习方法带来了一个全新的以工程为中心的视角来解决我们提出的长期困难的神经技术问题。我们的机器学习方法在医疗保健创新和开发新的脑机接口设备方面显示出巨大的前景。此外，我们提出的机器学习工具不仅对我们实现目标至关重要，而且还将为机器学习社区提供新的算法和技术。因此，这项研究为加拿大提供了通过加强和改进脑研究中ML方法的使用来维护其在神经技术领域存在的潜力。***********