Machine Learning and Signal Processing for Advances in Neurotechnology

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