Publications
2013
Daly, Ian; Sweeney-Reed, Catherine; Nasuto, Slawomir J.
Testing for significance of phase synchronisation dynamics in the EEG Journal Article
In: Journal of Computational Neuroscience, vol. 34, no. 3, pp. 411-432, 2013.
Abstract | Links | BibTeX | Tags: EEG, Functional connectivity, Markov models, Semi-Markov models, Significance testing
@article{Daly2012phase,
title = {Testing for significance of phase synchronisation dynamics in the EEG},
author = {Ian Daly and Catherine Sweeney-Reed and Slawomir J. Nasuto},
url = {http://www.iandaly.co.uk/wp-content/uploads/2016/01/Testing-for-significance-of-phase-synchronisation-dynamics-in.pdf},
doi = {10.1007/s10827-012-0428-2},
year = {2013},
date = {2013-06-01},
journal = {Journal of Computational Neuroscience},
volume = {34},
number = {3},
pages = {411-432},
abstract = {A number of tests exist to check for statistical significance of phase synchronisation within the Electroencephalogram (EEG); however, the majority suffer from a lack of generality and applicability. They may also fail to account for temporal dynamics in the phase synchronisation, regarding synchronisation as a constant state instead of a dynamical process. Therefore, a novel test is developed for identifying the statistical significance of phase synchronisation based upon a combination of work characterising temporal dynamics of multivariate time-series and Markov modelling. We show how this method is better able to assess the significance of phase synchronisation than a range of commonly used significance tests. We also show how the method may be applied to identify and classify significantly different phase synchronisation dynamics in both univariate and multivariate datasets.},
keywords = {EEG, Functional connectivity, Markov models, Semi-Markov models, Significance testing},
pubstate = {published},
tppubtype = {article}
}
A number of tests exist to check for statistical significance of phase synchronisation within the Electroencephalogram (EEG); however, the majority suffer from a lack of generality and applicability. They may also fail to account for temporal dynamics in the phase synchronisation, regarding synchronisation as a constant state instead of a dynamical process. Therefore, a novel test is developed for identifying the statistical significance of phase synchronisation based upon a combination of work characterising temporal dynamics of multivariate time-series and Markov modelling. We show how this method is better able to assess the significance of phase synchronisation than a range of commonly used significance tests. We also show how the method may be applied to identify and classify significantly different phase synchronisation dynamics in both univariate and multivariate datasets.
2012
Billinger, Martin; Daly, Ian; Kaiser, Vera; Jin, Jing; Allison, Brendan Z.; Müller-Putz, Gernot R.; Brunner, Clemens
Is it Significant? Guidelines for Reporting BCI Performance Book Chapter
In: Stephen Dunne Brendan Z. Allison, Robert Leeb (Ed.): pp. 333-354, Springer, 2012, ISBN: 978-3-642-29745-8.
Abstract | Links | BibTeX | Tags: BCI, Classification, Information transfer rate, Significance testing
@inbook{Billinger2012,
title = {Is it Significant? Guidelines for Reporting BCI Performance},
author = {Martin Billinger and Ian Daly and Vera Kaiser and Jing Jin and Brendan Z. Allison and Gernot R. Müller-Putz and Clemens Brunner},
editor = {Brendan Z. Allison, Stephen Dunne, Robert Leeb, José Del R. Millán, Anton Nijholt},
url = {http://link.springer.com/chapter/10.1007%2F978-3-642-29746-5_17},
doi = {10.1007/978-3-642-29746-5_17},
isbn = {978-3-642-29745-8},
year = {2012},
date = {2012-07-07},
pages = {333-354},
publisher = {Springer},
abstract = {Recent growth in brain-computer interface (BCI) research has increased pressure to report improved performance. However, different research groups report performance in different ways. Hence, it is essential that evaluation procedures are valid and reported in sufficient detail. In this chapter we give an overview of available performance measures such as classification accuracy, cohen’s kappa, information transfer rate (ITR), and written symbol rate. We show how to distinguish results from chance level using confidence intervals for accuracy or kappa. Furthermore, we point out common pitfalls when moving from offline to online analysis and provide a guide on how to conduct statistical tests on (BCI) results.},
keywords = {BCI, Classification, Information transfer rate, Significance testing},
pubstate = {published},
tppubtype = {inbook}
}
Recent growth in brain-computer interface (BCI) research has increased pressure to report improved performance. However, different research groups report performance in different ways. Hence, it is essential that evaluation procedures are valid and reported in sufficient detail. In this chapter we give an overview of available performance measures such as classification accuracy, cohen’s kappa, information transfer rate (ITR), and written symbol rate. We show how to distinguish results from chance level using confidence intervals for accuracy or kappa. Furthermore, we point out common pitfalls when moving from offline to online analysis and provide a guide on how to conduct statistical tests on (BCI) results.
2011
Daly, Ian
Phase Synchronisation in Brain Computer Interfacing PhD Thesis
School of Systems Engineering, 2011.
Abstract | Links | BibTeX | Tags: Artefact removal, BCI, EEG, Feature selection, Functional connectivity, Machine learning, Neural mass models, Phase synchronisation, PhD, Significance testing, Thesis
@phdthesis{Daly2011a,
title = {Phase Synchronisation in Brain Computer Interfacing},
author = {Ian Daly},
url = {http://www.iandaly.co.uk/publications/thesis/Phase_Synchronisation_in_Brain_Computer_Interfacing.pdf},
year = {2011},
date = {2011-07-01},
pages = {1-262},
address = {University of Reading},
school = {School of Systems Engineering},
abstract = {Brain Computer Interfaces (BCIs) are an emerging area of research combining the Neuroscience, Computer Science, Engineering, Mathematics, Human Computer Interaction and Psychology research fields. A BCI enables an individual to exert control of a computer without activation of the efferent nervous system or the muscles. This allows individuals suffering with partial or complete paralysis and associated conditions which prevent muscle movement to control a computer and hence communicate and exert control over their environment.
This thesis first investigates tools for automatically removing artifacts from the Electroencephalogram (EEG), a signal commonly used in the control a BCI. Tools for measuring inter-regional connectivity patterns within the brain via phase synchronisation are then evaluated and extended to provide novel measures of inter-regional connectivity across the entire cortex.
Feature selection approaches are then introduced and evaluated before being applied to select good feature sets for the discrimination of connectivity patterns. These approaches are compared to Markov modelling approaches which model
and classify temporal dependencies in the data.
The resulting tool-set is applied to a novel BCI control paradigm based upon the detection of single finger taps. It is demonstrated that the connectivity features produce significantly better classification accuracies than can be achieved using conventional features traditionally applied in BCI.},
type = {PhD Thesis},
keywords = {Artefact removal, BCI, EEG, Feature selection, Functional connectivity, Machine learning, Neural mass models, Phase synchronisation, PhD, Significance testing, Thesis},
pubstate = {published},
tppubtype = {phdthesis}
}
Brain Computer Interfaces (BCIs) are an emerging area of research combining the Neuroscience, Computer Science, Engineering, Mathematics, Human Computer Interaction and Psychology research fields. A BCI enables an individual to exert control of a computer without activation of the efferent nervous system or the muscles. This allows individuals suffering with partial or complete paralysis and associated conditions which prevent muscle movement to control a computer and hence communicate and exert control over their environment.
This thesis first investigates tools for automatically removing artifacts from the Electroencephalogram (EEG), a signal commonly used in the control a BCI. Tools for measuring inter-regional connectivity patterns within the brain via phase synchronisation are then evaluated and extended to provide novel measures of inter-regional connectivity across the entire cortex.
Feature selection approaches are then introduced and evaluated before being applied to select good feature sets for the discrimination of connectivity patterns. These approaches are compared to Markov modelling approaches which model
and classify temporal dependencies in the data.
The resulting tool-set is applied to a novel BCI control paradigm based upon the detection of single finger taps. It is demonstrated that the connectivity features produce significantly better classification accuracies than can be achieved using conventional features traditionally applied in BCI.
This thesis first investigates tools for automatically removing artifacts from the Electroencephalogram (EEG), a signal commonly used in the control a BCI. Tools for measuring inter-regional connectivity patterns within the brain via phase synchronisation are then evaluated and extended to provide novel measures of inter-regional connectivity across the entire cortex.
Feature selection approaches are then introduced and evaluated before being applied to select good feature sets for the discrimination of connectivity patterns. These approaches are compared to Markov modelling approaches which model
and classify temporal dependencies in the data.
The resulting tool-set is applied to a novel BCI control paradigm based upon the detection of single finger taps. It is demonstrated that the connectivity features produce significantly better classification accuracies than can be achieved using conventional features traditionally applied in BCI.