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Deep Learning for Magnetoencephalography

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thesis
posted on 15.09.2022, 01:50 authored by Tim Chard

Deep learning has been used in a wide range of applications, but it has only very recently been applied to Magnetoencephalography which is used to understand a variety of cognitive processes; for instance it can be used to understand how we process language or identify cognitive decline such as dementia. Work published in 2019 showed that it was possible to apply deep learning to categorise induced responses to stimuli across subjects. While trailblazing in its application of deep learning, it used relatively simple neural network (NN) models compared to other domains such as image and natural language processing.

In these other domains, there is a long history in developing complex NN models that combine spatial and temporal information in a range of ways. This thesis proposes more complex NN models that focus on modeling temporal relationships in the data, and applies them to the challenges of MEG data such as vulnerability to noise. In addition, it explores other insights from image processing to this domain, such as the unexpectedly high importance of approaches to data normalization. It applies these techniques to an extended range of MEG-based tasks, and finds that our new NN models outperform existing work on temporally-oriented tasks.

History

Table of Contents

1 Introduction -- 2 Background -- 3 Foundation -- 4 New Models -- 5 Model Preprocessing -- 6 Conclusion -- A Appendix -- References

Notes

A thesis submitted to Macquarie University for the degree of Master of Research

Awarding Institution

Macquarie University

Degree Type

Thesis MRes

Degree

Thesis (MRes), Macquarie University, Faculty of Science and Engineering, 2021

Department, Centre or School

Department of Computing

Year of Award

2021

Principal Supervisor

Mark Dras

Rights

Copyright: The Author Copyright disclaimer: https://www.mq.edu.au/copyright-disclaimer

Language

English

Extent

56 pages