The design and implementation of novel computational and machine learning approaches for modelling brain dynamics: towards more interpretable and real-time brain analysis

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.