Escudero Rodriguez, Javier

Polydorides, Nicholas

Auyeung, Bonnie

Kinney-Lang, Eli W.

2019-03-18T11:29:18Z

2019-03-18T11:29:18Z

2019-07-03

This doctoral thesis outlines advances in multi-way analysis for characterizing electroencephalogram (EEG) recordings from a paediatric population, with the aim to describe new links between EEG data and changes in the brain. This entails establishing the validity of multi-way analysis as a framework for identifying developmental information at the individual and collective level. Multi-way analysis broadens matrix analysis to a multi-linear algebraic architecture to identify latent structural relationships in naturally occurring higher order (n-way) data, like EEG. We use the canonical polyadic decomposition (CPD) as a multi-way model to efficiently express the complex structures present in paediatric EEG recordings as unique combinations of low-rank matrices, offering new insights into child development. This multi-way CPD framework is explored for both typically developing (TD) children and children with potential developmental delays (DD), e.g. children who suffer from epilepsy or paediatric stroke. Resting-state EEG (rEEG) data serves as an intuitive starting point in analyzing paediatric EEG via multi-way analysis. Here, the CPD model probes the underlying relationships between the spatial, spectral and subject modes of several rEEG datasets. We demonstrate the CPD can reveal distinct population-level features in rEEG that reflect unique developmental traits in varying child populations. These development-affiliated profiles are evaluated with respect to capturing structures well-established in childhood EEG. The identified features are also interrogated for their predictive abilities in anticipating new subjects’ ages. Assessing simulations and real rEEG datasets of TD and DD children establishes the multi-way analysis framework as well suited for identifying developmental profiles from paediatric rEEG. We extend the multi-way analysis scheme to more complex EEG scenarios common in EEG rehabilitation technology, like brain-computer interfaces. We explore the feasibility of multi-way modelling for interventions where developmental changes often pose as barriers. The multi-way CPD model is expanded to include four modes- task, spatial, spectral and subject data, with non-negativity and orthogonality constraints imposed. We analyze a visual attention task that elucidates a steady-state visual evoked potential and present the advantages gained from the extended CPD model. Through direct multi-linear projection, we demonstrate that linear profiles of the CPD can be capitalized upon for rapid task classification sans individual subject classifier calibration. Incorporating concepts from the multi-way analysis scheme with child development measured by psychometric tests, we propose the Joint EEG Development Inference (JEDI) model for inferring development from paediatric EEG. We utilize a common EEG task (button-press) to establish a 4-way CPD model of paediatric EEG data. Structured data fusion of the CPD model and cognitive scores from psychometric evaluations then permits joint decomposition of the two datasets to identify common features associated with each representation of development. Use of grid search optimization and a fully cross-validated design supports the JEDI model as another technique for rapidly discerning the developmental status of a child via EEG. We then briefly turn our attention to associating child development as measured by psychometric tests to markers in the EEG using graph network properties. Using graph networks, we show how the functional connectivity can inform on potential developmental delays in very young epileptic children using routine, clinical rEEG measures. This establishes a potential tool complementary to the JEDI model for identifying and inferring links between the established psychometric evaluation of developing children and functional analysis of the EEG. Multi-way analysis of paediatric EEG data offers a new approach for handling the developmental status and profiles of children. The CPD model offers flexibility in terms of identifying development-related features, and can be integrated into EEG tasks common in rehabilitation paradigms. We aim for the multi-way framework and associated techniques pursued in this thesis to be integrated and adopted as a useful tool clinicians can use for characterizing paediatric development.

http://hdl.handle.net/1842/35591

en

The University of Edinburgh

Kinney-Lang, E., Auyeung, B. and Escudero, J., 2016. Expanding the (kaleido) scope: exploring current literature trends for translating electroencephalography (EEG) based brain–computer interfaces for motor rehabilitation in children. Journal of neural engineering, 13(6), p.061002

Kinney-Lang, E., Spyrou, L., Ebied, A., Chin, R.F. and Escudero, J., 2018. Tensor-driven extraction of developmental features from varying paediatric EEG datasets. Journal of neural engineering, 15(4), p.046024

Kinney-Lang, E., Ebied, A., Auyeung, B., Chin, R.F., and Escudero, J., 2018. Introducing the Joint EEG-Development Inference (JEDI) Model: A Multi-way, Data Fusion Approach for Estimating Paediatric Developmental Scores Via EEG. IEEE Transactions on neural systems & rehabilitation engineering,

Kinney-Lang, E., Yoong, M., Hunter, M., Tallur, K.K., Shetty, J., McLellan, A., Chin, R.F. and Escudero, J., 2017. Analysis of EEG networks and their correlation with cognitive impairment in preschool children with epilepsy. Epilepsy & Behaviour, 90, p.45-56

Kinney-Lang, E., Spyrou, L., Ebied, A., Chin, R. and Escudero, J., 2017, July. Elucidating age-specific patterns from background electroencephalogram pediatric datasets via PARAFAC. In Engineering in Medicine and Biology Society (EMBC), 2017 39th Annual International Conference of the IEEE (pp. 3797-3800). IEEE

Kinney-Lang, E., Ebied, A., and Escudero, J., 2018. Building a Tensor Framework for the Analysis and Classification of Steady-State Visual Evoked Potentials in Children. Eusipco’18: Proceedings of the 26th European Signal Processing Conference. ISBN 978-90-827970-1-5. EURASIP 2018

electroencephalogram recordings

EEG data

canonical polyadic decomposition

CPD

paediatric EEG recordings

CPD framework

rEEG

development-affiliated profiles

JEDI model

graph networks

CPD model

Applications of multi-way analysis for characterizing paediatric electroencephalogram (EEG) recordings

Thesis or Dissertation

Doctoral

PhD Doctor of Philosophy