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

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

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

Exploring CPD based unsupervised classification for auditory BCI with mobile EEG

© 2015 IEEE. The analysis of mobile EEG Brain Computer Interface (BCI) recordings can benefit from unsupervised learning methods. Removing the calibration phase allows for faster and shorter interactions with a BCI and could potentially deal with non-stationarity issues in the signal quality. Here we present a data-driven approach based on a trilinear decomposition, Canonical Polyadic Decomposition (CPD), applied to an auditory BCI dataset. Different ways to construct a data-tensor for this purpose and how the results can be interpreted are explained. We also discuss current limitations in terms of trial identification and model initialization. The results of the new analysis are shown to be comparable to those of the traditional supervised stepwise LDA approach.status: publishe