Publications
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.
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.