Publications
2021
Daly, Ian
Removal of physiological artifacts from simultaneous EEG and fMRI recordings Journal Article
In: Clinical Neurophysiology, 2021.
BibTeX | Tags: Affective computing, Artefact removal, Classification, EEG, fMRI
@article{Daly2021Art,
title = {Removal of physiological artifacts from simultaneous EEG and fMRI recordings},
author = {Ian Daly},
year = {2021},
date = {2021-06-01},
journal = {Clinical Neurophysiology},
keywords = {Affective computing, Artefact removal, Classification, EEG, fMRI},
pubstate = {published},
tppubtype = {article}
}
2015
Daly, Ian; Billinger, Martin; Scherer, Reinhold; Müller-Putz, Gernot
FORCe: Fully Online and automated artifact Removal for brain-Computer interfacing Journal Article
In: IEEE in Transactions on Neural Systems & Rehabilitation Engineering, vol. 23, no. 5, pp. 725-736, 2015.
Abstract | Links | BibTeX | Tags: Artefact removal, BCI, EEG, ICA, Tools, Wavelets
@article{Daly2014a,
title = {FORCe: Fully Online and automated artifact Removal for brain-Computer interfacing},
author = {Ian Daly and Martin Billinger and Reinhold Scherer and Gernot Müller-Putz},
url = {http://www.iandaly.co.uk/wp-content/uploads/2016/01/FORCe-fully-online-and-automated-artifact-removal-for-brain-computer-interfacing.pdf},
doi = {10.1109/TNSRE.2014.2346621},
year = {2015},
date = {2015-09-01},
journal = {IEEE in Transactions on Neural Systems & Rehabilitation Engineering},
volume = {23},
number = {5},
pages = {725-736},
abstract = {A fully automated and online artifact removal method for the electroencephalogram (EEG) is developed for use in brain-computer interfacing (BCI). The method (FORCe) is based upon a novel combination of wavelet decomposition, independent component analysis, and thresholding. FORCe is able to operate on a small channel set during online EEG acquisition and does not require additional signals (e.g., electrooculogram signals). Evaluation of FORCe is performed offline on EEG recorded from 13 BCI particpants with cerebral palsy (CP) and online with three healthy participants. The method outperforms the state-of the-art automated artifact removal methods Lagged Auto-Mutual Information Clustering (LAMIC) and Fully Automated Statistical Thresholding for EEG artifact Rejection (FASTER), and is able to remove a wide range of artifact types including blink, electromyogram (EMG), and electrooculogram (EOG) artifacts.},
keywords = {Artefact removal, BCI, EEG, ICA, Tools, Wavelets},
pubstate = {published},
tppubtype = {article}
}
A fully automated and online artifact removal method for the electroencephalogram (EEG) is developed for use in brain-computer interfacing (BCI). The method (FORCe) is based upon a novel combination of wavelet decomposition, independent component analysis, and thresholding. FORCe is able to operate on a small channel set during online EEG acquisition and does not require additional signals (e.g., electrooculogram signals). Evaluation of FORCe is performed offline on EEG recorded from 13 BCI particpants with cerebral palsy (CP) and online with three healthy participants. The method outperforms the state-of the-art automated artifact removal methods Lagged Auto-Mutual Information Clustering (LAMIC) and Fully Automated Statistical Thresholding for EEG artifact Rejection (FASTER), and is able to remove a wide range of artifact types including blink, electromyogram (EMG), and electrooculogram (EOG) artifacts.
2014
Bauernfeind, Gunther; Wriessnegger, Selina; Daly, Ian; Müller-Putz, Gernot
Separating heart and brain: On the reduction of physiological noise from multichannel functional near-infrared spectroscopy (fNIRS) signals Journal Article
In: Journal of Neural Engineering, vol. 11, no. 5, pp. 1-18, 2014.
Abstract | Links | BibTeX | Tags: Artefact removal, fNIRS, ICA, Mayer wave
@article{Bauernfeind2014,
title = {Separating heart and brain: On the reduction of physiological noise from multichannel functional near-infrared spectroscopy (fNIRS) signals},
author = {Gunther Bauernfeind and Selina Wriessnegger and Ian Daly and Gernot Müller-Putz},
doi = {10.1088/1741-2560/11/5/056010},
year = {2014},
date = {2014-09-11},
journal = {Journal of Neural Engineering},
volume = {11},
number = {5},
pages = {1-18},
abstract = {Objective. Functional near-infrared spectroscopy (fNIRS) is an emerging technique for the in vivo assessment of functional activity of the cerebral cortex as well as in the field of brain–computer interface (BCI) research. A common challenge for the utilization of fNIRS in these areas is a stable and reliable investigation of the spatio-temporal hemodynamic patterns. However, the recorded patterns may be influenced and superimposed by signals generated from physiological processes, resulting in an inaccurate estimation of the cortical activity. Up to now only a few studies have investigated these influences, and still less has been attempted to remove/reduce these influences. The present study aims to gain insights into the reduction of physiological rhythms in hemodynamic signals (oxygenated hemoglobin (oxy-Hb), deoxygenated hemoglobin (deoxy-Hb)). Approach. We introduce the use of three different signal processing approaches (spatial filtering, a common average reference (CAR) method; independent component analysis (ICA); and transfer function (TF) models) to reduce the influence of respiratory and blood pressure (BP) rhythms on the hemodynamic responses. Main results. All approaches produce large reductions in BP and respiration influences on the oxy-Hb signals and, therefore, improve the contrast-to-noise ratio (CNR). In contrast, for deoxy-Hb signals CAR and ICA did not improve the CNR. However, for the TF approach, a CNR-improvement in deoxy-Hb can also be found. Significance. The present study investigates the application of different signal processing approaches to reduce the influences of physiological rhythms on the hemodynamic responses. In addition to the identification of the best signal processing method, we also show the importance of noise reduction in fNIRS data.},
keywords = {Artefact removal, fNIRS, ICA, Mayer wave},
pubstate = {published},
tppubtype = {article}
}
Objective. Functional near-infrared spectroscopy (fNIRS) is an emerging technique for the in vivo assessment of functional activity of the cerebral cortex as well as in the field of brain–computer interface (BCI) research. A common challenge for the utilization of fNIRS in these areas is a stable and reliable investigation of the spatio-temporal hemodynamic patterns. However, the recorded patterns may be influenced and superimposed by signals generated from physiological processes, resulting in an inaccurate estimation of the cortical activity. Up to now only a few studies have investigated these influences, and still less has been attempted to remove/reduce these influences. The present study aims to gain insights into the reduction of physiological rhythms in hemodynamic signals (oxygenated hemoglobin (oxy-Hb), deoxygenated hemoglobin (deoxy-Hb)). Approach. We introduce the use of three different signal processing approaches (spatial filtering, a common average reference (CAR) method; independent component analysis (ICA); and transfer function (TF) models) to reduce the influence of respiratory and blood pressure (BP) rhythms on the hemodynamic responses. Main results. All approaches produce large reductions in BP and respiration influences on the oxy-Hb signals and, therefore, improve the contrast-to-noise ratio (CNR). In contrast, for deoxy-Hb signals CAR and ICA did not improve the CNR. However, for the TF approach, a CNR-improvement in deoxy-Hb can also be found. Significance. The present study investigates the application of different signal processing approaches to reduce the influences of physiological rhythms on the hemodynamic responses. In addition to the identification of the best signal processing method, we also show the importance of noise reduction in fNIRS data.
Bauernfeind, Günther; Wriessnegger, Selina; Daly, Ian; Müller-Putz, Gernot R.
Proceedings of the Graz Brain-computer interface conference 2014, 2014.
Links | BibTeX | Tags: Artefact removal, fNIRS, ICA
@conference{Bauernfeind2014conf,
title = {Physiological noise reduction from multichannel functional near-infrared spectroscopy (fNIRS) signals},
author = {Günther Bauernfeind and Selina Wriessnegger and Ian Daly and Gernot R. Müller-Putz},
url = {http://centaur.reading.ac.uk/40604/},
year = {2014},
date = {2014-09-01},
booktitle = {Proceedings of the Graz Brain-computer interface conference 2014},
keywords = {Artefact removal, fNIRS, ICA},
pubstate = {published},
tppubtype = {conference}
}
2013
Daly, Ian; Nicolaou, Nicoletta; Nasuto, Slawomir J.; Warwick, Kevin
Automated artifact removal from the electroencephalogram; a comparative study Journal Article
In: Clinical EEG and Neuroscience, vol. 44, no. 4, pp. 291-306, 2013.
Abstract | Links | BibTeX | Tags: Artefact removal, EEG, ICA, MSSA, Wavelets
@article{Daly2013artComp,
title = {Automated artifact removal from the electroencephalogram; a comparative study},
author = {Ian Daly and Nicoletta Nicolaou and Slawomir J. Nasuto and Kevin Warwick},
url = {http://www.iandaly.co.uk/wp-content/uploads/2016/01/published_MS_authors_copy.pdf},
doi = {10.1177/1550059413476485},
year = {2013},
date = {2013-10-01},
journal = {Clinical EEG and Neuroscience},
volume = {44},
number = {4},
pages = {291-306},
abstract = {Contamination of the electroencephalogram (EEG) by artifacts greatly reduces the quality of the recorded signals. There is a need for automated artifact removal methods. However, such methods are rarely evaluated against one another via rigorous criteria, with results often presented based upon visual inspection alone. This work presents a comparative study of automatic methods for removing blink, electrocardiographic, and electromyographic artifacts from the EEG. Three methods are considered; wavelet, blind source separation (BSS), and multivariate singular spectrum analysis (MSSA)-based correction. These are applied to data sets containing mixtures of artifacts. Metrics are devised to measure the performance of each method. The BSS method is seen to be the best approach for artifacts of high signal to noise ratio (SNR). By contrast, MSSA performs well at low SNRs but at the expense of a large number of false positive corrections.},
keywords = {Artefact removal, EEG, ICA, MSSA, Wavelets},
pubstate = {published},
tppubtype = {article}
}
Contamination of the electroencephalogram (EEG) by artifacts greatly reduces the quality of the recorded signals. There is a need for automated artifact removal methods. However, such methods are rarely evaluated against one another via rigorous criteria, with results often presented based upon visual inspection alone. This work presents a comparative study of automatic methods for removing blink, electrocardiographic, and electromyographic artifacts from the EEG. Three methods are considered; wavelet, blind source separation (BSS), and multivariate singular spectrum analysis (MSSA)-based correction. These are applied to data sets containing mixtures of artifacts. Metrics are devised to measure the performance of each method. The BSS method is seen to be the best approach for artifacts of high signal to noise ratio (SNR). By contrast, MSSA performs well at low SNRs but at the expense of a large number of false positive corrections.
Daly, Ian; Billinger, Martin; Scherer, Reinhold; Muller-Putz, Gernot
On the automated removal of artifacts related to head movement from the EEG Journal Article
In: IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 21, no. 3, pp. 427 - 434, 2013, ISSN: 1534-4320.
Abstract | Links | BibTeX | Tags: Artefact removal, EEG, EMG, Head movement, ICA
@article{Daly2013headArtifacts,
title = {On the automated removal of artifacts related to head movement from the EEG},
author = {Ian Daly and Martin Billinger and Reinhold Scherer and Gernot Muller-Putz},
url = {http://www.iandaly.co.uk/newDesign2016/wp-content/uploads/2016/01/On-the-automated-removal-of-artifacts-related-to-head-movement-from-the-EEG.pdf},
doi = {10.1109/TNSRE.2013.2254724},
issn = {1534-4320},
year = {2013},
date = {2013-05-01},
journal = {IEEE Transactions on Neural Systems and Rehabilitation Engineering},
volume = {21},
number = {3},
pages = {427 - 434},
abstract = {Contamination of the electroencephalogram (EEG) by artifacts related to head movement is a major cause of reduced signal quality. This is a problem in both neuroscience and other uses of the EEG. To attempt to reduce the influence, on the EEG, of artifacts related to head movement, an accelerometer is placed on the head and independent component analysis is applied to attempt to separate artifacts which are statistically related to head movements. To evaluate the method, EEG and accelerometer measurements are made from 14 individuals with Cerebral palsy attempting to control a sensorimotor rhythm based brain-computer interface. Results show that the approach significantly reduces the influence of head movement related artifacts in the EEG.},
keywords = {Artefact removal, EEG, EMG, Head movement, ICA},
pubstate = {published},
tppubtype = {article}
}
Contamination of the electroencephalogram (EEG) by artifacts related to head movement is a major cause of reduced signal quality. This is a problem in both neuroscience and other uses of the EEG. To attempt to reduce the influence, on the EEG, of artifacts related to head movement, an accelerometer is placed on the head and independent component analysis is applied to attempt to separate artifacts which are statistically related to head movements. To evaluate the method, EEG and accelerometer measurements are made from 14 individuals with Cerebral palsy attempting to control a sensorimotor rhythm based brain-computer interface. Results show that the approach significantly reduces the influence of head movement related artifacts in the EEG.
2012
Bauernfeind, Günther; Daly, Ian; Müller-Putz, Gernot
On the removal of physiological artifacts from fNIRS Conference
Proceedings of the 3rd TOBI workshop, Würzburg, Germany, 2012.
Abstract | BibTeX | Tags: Artefact removal, fNIRS, ICA, Mayer wave
@conference{Bauernfeind2012,
title = {On the removal of physiological artifacts from fNIRS},
author = {Günther Bauernfeind and Ian Daly and Gernot Müller-Putz},
year = {2012},
date = {2012-10-01},
booktitle = {Proceedings of the 3rd TOBI workshop, Würzburg, Germany},
abstract = {In the present study we report on the reduction of physiological rhythms in hemodynamic signals recorded with functional near - infrared spectroscopy (fNIRS). We investigated the use of two different signal processing approaches to reduce the influence of respiratory and blood pressure rhythms (Mayer waves) on the hemodynamic responses.},
keywords = {Artefact removal, fNIRS, ICA, Mayer wave},
pubstate = {published},
tppubtype = {conference}
}
In the present study we report on the reduction of physiological rhythms in hemodynamic signals recorded with functional near - infrared spectroscopy (fNIRS). We investigated the use of two different signal processing approaches to reduce the influence of respiratory and blood pressure rhythms (Mayer waves) on the hemodynamic responses.
Daly, Ian; Pichiorri, Floriana; Faller, Josef; Kaiser, Vera; Kreilinger, Alex; Scherer, Reinhold; Müller-Putz, Gernot
What does clean EEG look like? Conference
Conf Proc IEEE Eng Med Biol Soc., IEEE, 2012, ISBN: 978-1-4244-4119-8.
Abstract | Links | BibTeX | Tags: Artefact removal, Differential evolution, EEG, Quality metrics
@conference{Daly2012b,
title = {What does clean EEG look like?},
author = {Ian Daly and Floriana Pichiorri and Josef Faller and Vera Kaiser and Alex Kreilinger and Reinhold Scherer and Gernot Müller-Putz},
url = {http://www.iandaly.co.uk/wp-content/uploads/2016/01/What-does-clean-EEG-look-like.pdf},
doi = {10.1109/EMBC.2012.6346834},
isbn = {978-1-4244-4119-8},
year = {2012},
date = {2012-06-01},
booktitle = {Conf Proc IEEE Eng Med Biol Soc.},
pages = {3963-3966},
publisher = {IEEE},
abstract = {Lack of a clear analytical metric for identifying artifact free, clean electroencephalographic (EEG) signals inhibits robust comparison of different artifact removal methods and lowers confidence in the results of EEG analysis. An algorithm is presented for identifying clean EEG epochs by thresholding statistical properties of the EEG. Thresholds are trained on EEG datasets from both healthy subjects and stroke / spinal cord injury patient populations via differential evolution (DE).},
keywords = {Artefact removal, Differential evolution, EEG, Quality metrics},
pubstate = {published},
tppubtype = {conference}
}
Lack of a clear analytical metric for identifying artifact free, clean electroencephalographic (EEG) signals inhibits robust comparison of different artifact removal methods and lowers confidence in the results of EEG analysis. An algorithm is presented for identifying clean EEG epochs by thresholding statistical properties of the EEG. Thresholds are trained on EEG datasets from both healthy subjects and stroke / spinal cord injury patient populations via differential evolution (DE).
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.