Publications
2019
Cheng, J; Jin, J; Daly, I; Zhang, Y; Wang, B; Wang, X; Cichocki, A
Effect of a combination of flip and zooming stimuli on the performance of a visual brain-computer interface for spelling Journal Article
In: Biomedical Engineering/Biomedizinische Technik, vol. 64, no. 1, pp. 29-38, 2019.
BibTeX | Tags: BCI, ERP, Event-related potential, Event-related potentials
@article{Cheng2019,
title = {Effect of a combination of flip and zooming stimuli on the performance of a visual brain-computer interface for spelling},
author = {J Cheng and J Jin and I Daly and Y Zhang and B Wang and X Wang and A Cichocki},
year = {2019},
date = {2019-01-14},
journal = {Biomedical Engineering/Biomedizinische Technik},
volume = {64},
number = {1},
pages = {29-38},
keywords = {BCI, ERP, Event-related potential, Event-related potentials},
pubstate = {published},
tppubtype = {article}
}
2016
Huang, Minqiang; Daly, Ian; Jin, Jing; Zhang, Yu; Wang, Xingyu; Cichocki, Andrzej
An exploration of spatial auditory BCI paradigms with different sounds: Music vs Beeps Journal Article
In: Cognitive Neurodynamics, 2016.
BibTeX | Tags: BCI, Event-related potentials
@article{Huang2016,
title = {An exploration of spatial auditory BCI paradigms with different sounds: Music vs Beeps},
author = {Minqiang Huang and Ian Daly and Jing Jin and Yu Zhang and Xingyu Wang and Andrzej Cichocki},
year = {2016},
date = {2016-01-20},
journal = {Cognitive Neurodynamics},
keywords = {BCI, Event-related potentials},
pubstate = {published},
tppubtype = {article}
}
2014
Jin, Jing; Daly, Ian; Zhang, Yu; Wang, Xingyu; Cichocki, Andrzej
An optimized ERP Brain-computer interface based on facial expression changes Journal Article
In: Journal of Neural Engineering, vol. 11, no. 3, pp. 1-11, 2014.
Abstract | Links | BibTeX | Tags: BCI, Event-related potentials, Facial expressions
@article{Jin2014,
title = {An optimized ERP Brain-computer interface based on facial expression changes},
author = {Jing Jin and Ian Daly and Yu Zhang and Xingyu Wang and Andrzej Cichocki},
url = {http://iopscience.iop.org/article/10.1088/1741-2560/11/3/036004/pdf},
doi = {10.1088/1741-2560/11/3/036004},
year = {2014},
date = {2014-06-01},
journal = {Journal of Neural Engineering},
volume = {11},
number = {3},
pages = {1-11},
abstract = {OBJECTIVE:
Interferences from spatially adjacent non-target stimuli are known to evoke event-related potentials (ERPs) during non-target flashes and, therefore, lead to false positives. This phenomenon was commonly seen in visual attention-based brain-computer interfaces (BCIs) using conspicuous stimuli and is known to adversely affect the performance of BCI systems. Although users try to focus on the target stimulus, they cannot help but be affected by conspicuous changes of the stimuli (such as flashes or presenting images) which were adjacent to the target stimulus. Furthermore, subjects have reported that conspicuous stimuli made them tired and annoyed. In view of this, the aim of this study was to reduce adjacent interference, annoyance and fatigue using a new stimulus presentation pattern based upon facial expression changes. Our goal was not to design a new pattern which could evoke larger ERPs than the face pattern, but to design a new pattern which could reduce adjacent interference, annoyance and fatigue, and evoke ERPs as good as those observed during the face pattern.
APPROACH:
Positive facial expressions could be changed to negative facial expressions by minor changes to the original facial image. Although the changes are minor, the contrast is big enough to evoke strong ERPs. In this paper, a facial expression change pattern between positive and negative facial expressions was used to attempt to minimize interference effects. This was compared against two different conditions, a shuffled pattern containing the same shapes and colours as the facial expression change pattern, but without the semantic content associated with a change in expression, and a face versus no face pattern. Comparisons were made in terms of classification accuracy and information transfer rate as well as user supplied subjective measures.
MAIN RESULTS:
The results showed that interferences from adjacent stimuli, annoyance and the fatigue experienced by the subjects could be reduced significantly (p < 0.05) by using the facial expression change patterns in comparison with the face pattern. The offline results show that the classification accuracy of the facial expression change pattern was significantly better than that of the shuffled pattern (p < 0.05) and the face pattern (p < 0.05).
SIGNIFICANCE:
The facial expression change pattern presented in this paper reduced interference from adjacent stimuli and decreased the fatigue and annoyance experienced by BCI users significantly (p < 0.05) compared to the face pattern.},
keywords = {BCI, Event-related potentials, Facial expressions},
pubstate = {published},
tppubtype = {article}
}
OBJECTIVE:
Interferences from spatially adjacent non-target stimuli are known to evoke event-related potentials (ERPs) during non-target flashes and, therefore, lead to false positives. This phenomenon was commonly seen in visual attention-based brain-computer interfaces (BCIs) using conspicuous stimuli and is known to adversely affect the performance of BCI systems. Although users try to focus on the target stimulus, they cannot help but be affected by conspicuous changes of the stimuli (such as flashes or presenting images) which were adjacent to the target stimulus. Furthermore, subjects have reported that conspicuous stimuli made them tired and annoyed. In view of this, the aim of this study was to reduce adjacent interference, annoyance and fatigue using a new stimulus presentation pattern based upon facial expression changes. Our goal was not to design a new pattern which could evoke larger ERPs than the face pattern, but to design a new pattern which could reduce adjacent interference, annoyance and fatigue, and evoke ERPs as good as those observed during the face pattern.
APPROACH:
Positive facial expressions could be changed to negative facial expressions by minor changes to the original facial image. Although the changes are minor, the contrast is big enough to evoke strong ERPs. In this paper, a facial expression change pattern between positive and negative facial expressions was used to attempt to minimize interference effects. This was compared against two different conditions, a shuffled pattern containing the same shapes and colours as the facial expression change pattern, but without the semantic content associated with a change in expression, and a face versus no face pattern. Comparisons were made in terms of classification accuracy and information transfer rate as well as user supplied subjective measures.
MAIN RESULTS:
The results showed that interferences from adjacent stimuli, annoyance and the fatigue experienced by the subjects could be reduced significantly (p < 0.05) by using the facial expression change patterns in comparison with the face pattern. The offline results show that the classification accuracy of the facial expression change pattern was significantly better than that of the shuffled pattern (p < 0.05) and the face pattern (p < 0.05).
SIGNIFICANCE:
The facial expression change pattern presented in this paper reduced interference from adjacent stimuli and decreased the fatigue and annoyance experienced by BCI users significantly (p < 0.05) compared to the face pattern.
Interferences from spatially adjacent non-target stimuli are known to evoke event-related potentials (ERPs) during non-target flashes and, therefore, lead to false positives. This phenomenon was commonly seen in visual attention-based brain-computer interfaces (BCIs) using conspicuous stimuli and is known to adversely affect the performance of BCI systems. Although users try to focus on the target stimulus, they cannot help but be affected by conspicuous changes of the stimuli (such as flashes or presenting images) which were adjacent to the target stimulus. Furthermore, subjects have reported that conspicuous stimuli made them tired and annoyed. In view of this, the aim of this study was to reduce adjacent interference, annoyance and fatigue using a new stimulus presentation pattern based upon facial expression changes. Our goal was not to design a new pattern which could evoke larger ERPs than the face pattern, but to design a new pattern which could reduce adjacent interference, annoyance and fatigue, and evoke ERPs as good as those observed during the face pattern.
APPROACH:
Positive facial expressions could be changed to negative facial expressions by minor changes to the original facial image. Although the changes are minor, the contrast is big enough to evoke strong ERPs. In this paper, a facial expression change pattern between positive and negative facial expressions was used to attempt to minimize interference effects. This was compared against two different conditions, a shuffled pattern containing the same shapes and colours as the facial expression change pattern, but without the semantic content associated with a change in expression, and a face versus no face pattern. Comparisons were made in terms of classification accuracy and information transfer rate as well as user supplied subjective measures.
MAIN RESULTS:
The results showed that interferences from adjacent stimuli, annoyance and the fatigue experienced by the subjects could be reduced significantly (p < 0.05) by using the facial expression change patterns in comparison with the face pattern. The offline results show that the classification accuracy of the facial expression change pattern was significantly better than that of the shuffled pattern (p < 0.05) and the face pattern (p < 0.05).
SIGNIFICANCE:
The facial expression change pattern presented in this paper reduced interference from adjacent stimuli and decreased the fatigue and annoyance experienced by BCI users significantly (p < 0.05) compared to the face pattern.
2013
Jin, Jing; Sellers, Eric W.; Zhang, Yu; Daly, Ian; Wang, Xingyu; Cichocki, Andrzej
Whether generic model works for rapid ERP-based BCI calibration Journal Article
In: Journal of Neuroscience Methods, vol. 212, no. 1, pp. 94-99, 2013.
Abstract | Links | BibTeX | Tags: BCI, Classification, Event-related potentials, Generic model
@article{Jin2012,
title = {Whether generic model works for rapid ERP-based BCI calibration},
author = {Jing Jin and Eric W. Sellers and Yu Zhang and Ian Daly and Xingyu Wang and Andrzej Cichocki},
doi = {10.1016/j.jneumeth.2012.09.020},
year = {2013},
date = {2013-01-13},
journal = {Journal of Neuroscience Methods},
volume = {212},
number = {1},
pages = {94-99},
abstract = {Event-related potential (ERP)-based brain-computer interfacing (BCI) is an effective method of basic communication. However, collecting calibration data, and classifier training, detracts from the amount of time allocated for online communication. Decreasing calibration time can reduce preparation time thereby allowing for additional online use, potentially lower fatigue, and improved performance. Previous studies, using generic online training models which avoid offline calibration, afford more time for online spelling. Such studies have not examined the direct effects of the model on individual performance, and the training sequence exceeded the time reported here. The first goal of this work is to survey whether one generic model works for all subjects and the second goal is to show the performance of a generic model using an online training strategy when participants could use the generic model. The generic model was derived from 10 participant's data. An additional 11 participants were recruited for the current study. Seven of the participants were able to use the generic model during online training. Moreover, the generic model performed as well as models obtained from participant specific offline data with a mean training time of less than 2 min. However, four of the participants could not use this generic model, which shows that one generic mode is not generic for all subjects. More research on ERPs of subjects with different characteristics should be done, which would be helpful to build generic models for subject groups. This result shows a potential valuable direction for improving the BCI system.},
keywords = {BCI, Classification, Event-related potentials, Generic model},
pubstate = {published},
tppubtype = {article}
}
Event-related potential (ERP)-based brain-computer interfacing (BCI) is an effective method of basic communication. However, collecting calibration data, and classifier training, detracts from the amount of time allocated for online communication. Decreasing calibration time can reduce preparation time thereby allowing for additional online use, potentially lower fatigue, and improved performance. Previous studies, using generic online training models which avoid offline calibration, afford more time for online spelling. Such studies have not examined the direct effects of the model on individual performance, and the training sequence exceeded the time reported here. The first goal of this work is to survey whether one generic model works for all subjects and the second goal is to show the performance of a generic model using an online training strategy when participants could use the generic model. The generic model was derived from 10 participant's data. An additional 11 participants were recruited for the current study. Seven of the participants were able to use the generic model during online training. Moreover, the generic model performed as well as models obtained from participant specific offline data with a mean training time of less than 2 min. However, four of the participants could not use this generic model, which shows that one generic mode is not generic for all subjects. More research on ERPs of subjects with different characteristics should be done, which would be helpful to build generic models for subject groups. This result shows a potential valuable direction for improving the BCI system.
2010
Daly, Ian; Williams, Nitin; Nasuto, Slawomir J.; Warwick, Kevin; Saddy, Doug
Single trial BCI operation via Wackermann parameters Conference
Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2010), IEEE, Kittila, Finland, 2010.
Abstract | Links | BibTeX | Tags: BCI, Event-related potentials, Network clustering, Single-trial classification, Wackermann parameters
@conference{Daly2010b,
title = {Single trial BCI operation via Wackermann parameters},
author = {Ian Daly and Nitin Williams and Slawomir J. Nasuto and Kevin Warwick and Doug Saddy},
url = {http://www.iandaly.co.uk/wp-content/uploads/2016/01/daly-et-al-Single-trial-BCI-operation-via-Wackermann-parameters.pdf},
year = {2010},
date = {2010-09-01},
booktitle = {Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2010)},
pages = {409-415},
publisher = {IEEE},
address = {Kittila, Finland},
abstract = {Accurate single trial P300 classification lends itself to fast
and accurate control of Brain Computer Interfaces (BCIs).
Highly accurate classification of single trial P300 ERPs is
achieved by characterizing the EEG via corresponding
stationary and time-varying Wackermann parameters. Subsets
of maximally discriminating parameters are then
selected using the Network Clustering feature selection
algorithm and classified with Naive-Bayes and Linear
Discriminant Analysis classifiers.
Hence the method is assessed on two different data-sets
from BCI competitions and is shown to produce accuracies
of between approximately 70% and 85%. This is promising
for the use of Wackermann parameters as features in the
classification of single-trial ERP responses.},
keywords = {BCI, Event-related potentials, Network clustering, Single-trial classification, Wackermann parameters},
pubstate = {published},
tppubtype = {conference}
}
Accurate single trial P300 classification lends itself to fast
and accurate control of Brain Computer Interfaces (BCIs).
Highly accurate classification of single trial P300 ERPs is
achieved by characterizing the EEG via corresponding
stationary and time-varying Wackermann parameters. Subsets
of maximally discriminating parameters are then
selected using the Network Clustering feature selection
algorithm and classified with Naive-Bayes and Linear
Discriminant Analysis classifiers.
Hence the method is assessed on two different data-sets
from BCI competitions and is shown to produce accuracies
of between approximately 70% and 85%. This is promising
for the use of Wackermann parameters as features in the
classification of single-trial ERP responses.
and accurate control of Brain Computer Interfaces (BCIs).
Highly accurate classification of single trial P300 ERPs is
achieved by characterizing the EEG via corresponding
stationary and time-varying Wackermann parameters. Subsets
of maximally discriminating parameters are then
selected using the Network Clustering feature selection
algorithm and classified with Naive-Bayes and Linear
Discriminant Analysis classifiers.
Hence the method is assessed on two different data-sets
from BCI competitions and is shown to produce accuracies
of between approximately 70% and 85%. This is promising
for the use of Wackermann parameters as features in the
classification of single-trial ERP responses.
2009
Williams, Nitin; Daly, Ian; Nasuto, Slawomir J.; Saddy, Doug; Warwick, Kevin
ERP classification using Empirical mode decomposition Conference
Proceedings of the Post-graduate conference on biomendical signal processing, Oxford, UK, 2009.
Links | BibTeX | Tags: EEG, Empirical mode decomposition, Event-related potentials
@conference{Williams2009a,
title = {ERP classification using Empirical mode decomposition},
author = {Nitin Williams and Ian Daly and Slawomir J. Nasuto and Doug Saddy and Kevin Warwick},
url = {http://www.iandaly.co.uk/wp-content/uploads/2016/01/ERP-classification-using-EMD.pdf},
year = {2009},
date = {2009-06-01},
booktitle = {Proceedings of the Post-graduate conference on biomendical signal processing, Oxford, UK},
keywords = {EEG, Empirical mode decomposition, Event-related potentials},
pubstate = {published},
tppubtype = {conference}
}
Daly, Ian; Nasuto, Slawomir J.; Warwick, Kevin
Phase resetting as a mechanism of ERP generation: evidence from the power spectrum Conference
Proceedings of the Post-graduate conference on biomendical signal processing, Oxford, UK, 2009.
Abstract | Links | BibTeX | Tags: EEG, Event-related potentials, Phase resetting
@conference{Daly2009c,
title = {Phase resetting as a mechanism of ERP generation: evidence from the power spectrum},
author = {Ian Daly and Slawomir J. Nasuto and Kevin Warwick},
url = {http://www.iandaly.co.uk/wp-content/uploads/2016/01/Phase-resetting-as-a-mechanism-of-ERP-generation.pdf},
year = {2009},
date = {2009-06-01},
booktitle = {Proceedings of the Post-graduate conference on biomendical signal processing, Oxford, UK},
abstract = {A Neural Mass model is coupled with a novel method to generate realistic Phase reset ERPs.
The power spectra of these synthetic ERPs are compared with the spectra of real ERPs and synthetic ERPs generated via the Additive model. Real ERP spectra show similarities with synthetic Phase reset ERPs and synthetic Additive ERPs.},
keywords = {EEG, Event-related potentials, Phase resetting},
pubstate = {published},
tppubtype = {conference}
}
A Neural Mass model is coupled with a novel method to generate realistic Phase reset ERPs.
The power spectra of these synthetic ERPs are compared with the spectra of real ERPs and synthetic ERPs generated via the Additive model. Real ERP spectra show similarities with synthetic Phase reset ERPs and synthetic Additive ERPs.
The power spectra of these synthetic ERPs are compared with the spectra of real ERPs and synthetic ERPs generated via the Additive model. Real ERP spectra show similarities with synthetic Phase reset ERPs and synthetic Additive ERPs.