Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.745280
Title: The design and implementation of novel computational and machine learning approaches for modelling brain dynamics : towards more interpretable and real-time brain analysis
Author: Dinov, Martin
ISNI:       0000 0004 7223 4340
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2018
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Abstract:
This thesis presents a combination of novel methods intended for improving Brain Computer Interface (BCI) use, such as a Dynamic Time Warping-based (DTW) spectrum, as well as new applications of existing methods, such as fuzzy clustering and neural networks, including reinforcement learning-driven Deep Q Networks (DQNs). We develop these mutually-compatible methods that aim to make brain analysis, especially in the context of BCIs, more interpretable and efficient. In Chapter 2, I developed a new approach based on using DTW towards computing frequency-domain spectra in a more interpretable way than using standard Fourier or Wavelet spectrums. Though it is applicable to any time series data, I applied the DTW-spectrum to EEG and show that it explains more variability in brain dynamics compared to other standard measures - most notably it seems to better predict certain benchmark measures of brain dynamics than the corresponding Fourier transforms. Chapter 3's main topic is using the fuzzy c-clustering and softmax neural network-based fully probabilistic classification and analysis framework that I developed for EEG microstates, which shows significant issues with standard deterministic analyses that have been used heretofore. Using a large publically available data set, I showed that imagined motor movements are less predictable than real motor movements. Further, I suggest that treating microstates as states representing a discrete dynamic of the brain is losing valuable information regarding the underlying dynamics. Chapter 4 is focused around Reinforcement Learning (RL)-driven Behavioral- and Neuro-Feedback (NFB) using phasic auditory alerts. This proof of concept work shows simulation and experimental results that suggest that the DQNs can learn meaningful behaviors with a portable consumer EEG within a small number of trials. The work necessitates a somewhat meandering journey through the relevant neuroscientific, mathematical and computational literature, which is covered by the background and foundation laid out in Chapter 1.
Supervisor: Leech, Robert ; Sharp, David Sponsor: Ministry of Defence ; Defence Science and Technology Laboratory
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.745280  DOI:
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