Machine Learning and Signal Processing for Advances in Neurotechnology

只要人类能够思考，我们就一直在努力了解自己的大脑。 然而，正如爱默生M。Pugh指出，“如果大脑是如此简单，我们可以理解它，我们将是如此简单，我们不能。尽管有这个固有的谜团，但近年来，人们一直在进行激烈的、多学科的活动，以了解人类大脑。神经科学家，生物化学家，心理学家和其他人已经在这个话题上投入了几十年，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，他们在推动这一领域的进步方面也可以发挥重要作用。大脑研究中的许多活动都可以用神经技术这个术语来概括。神经技术与旨在改善和修复大脑功能的技术的发展有关，或者促进人脑与计算机之间的直接交互，即，脑机接口（BCI）。在这种情况下，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一个诱人的前景是机器学习（ML）。 由于大脑过于复杂，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基本的数学模型来操作。申请人的团队以前曾使用脑电图（EEG）的ML分析来诊断精神疾病，也用于BCI应用。此外，申请人的团队是第一个开发新的ML方法来确定有效的抑郁症治疗方法的人。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方法来预测从昏迷中恢复。 我们还打算扩展我们新开发的ML算法，以增强对精神疾病的治疗。第三，我们打算开发改进的BCI设备，使用一种新的交互式训练方法，其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。 第一个这样的工具是扩展我们新开发的ML算法的能力，第二个是从代表大脑网络的EEG中识别新的高度突出的生物标志物。ML方法为解决我们提出的长期存在的神经技术难题带来了一个全新的以工程为中心的视角。我们的机器学习方法在医疗保健创新和开发新的BCI设备方面显示出巨大的潜力。此外，我们提出的ML工具不仅对我们实现目标至关重要，而且还将为ML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善ML方法在大脑研究中的使用来维护其在神经技术领域的存在的潜力。

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.***********