Publications
2012
Wagner, Isabella C.; Daly, Ian; Valjamae, Aleksander
Non-visual and Multisensory BCI Systems: Present and Future Book Chapter
In: Stephen Dunne Brendan Z. Allison, Robert Leeb (Ed.): pp. 375-393, Springer, 2012, ISBN: 978-3-642-29745-8.
Abstract | Links | BibTeX | Tags: BCI, Multi-modal BCI, P300, Speech BCI, Tactile BCI
@inbook{Wagner2012,
title = {Non-visual and Multisensory BCI Systems: Present and Future},
author = {Isabella C. Wagner and Ian Daly and Aleksander Valjamae},
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_19},
doi = {10.1007/978-3-642-29746-5_19},
isbn = {978-3-642-29745-8},
year = {2012},
date = {2012-07-07},
pages = {375-393},
publisher = {Springer},
abstract = {During the past decade, brain–computer interfaces (BCIs) have rapidly developed, both in technological and application domains. However, most of these interfaces rely on the visual modality. Only some research groups have been studying non-visual BCIs, primarily based on auditory and, sometimes, on somatosensory signals. These non-visual BCI approaches are especially useful for severely disabled patients with poor vision. From a broader perspective, multisensory BCIs may offer more versatile and user-friendly paradigms for control and feedback. This chapter describes current systems that are used within auditory and somatosensory BCI research. Four categories of noninvasive BCI paradigms are employed: (1) P300 evoked potentials, (2) steady-state evoked potentials, (3) slow cortical potentials, and (4) mental tasks. Comparing visual and non-visual BCIs, we propose and discuss different possible multisensory combinations, as well as their pros and cons. We conclude by discussing potential future research directions of multisensory BCIs and related research questions},
keywords = {BCI, Multi-modal BCI, P300, Speech BCI, Tactile BCI},
pubstate = {published},
tppubtype = {inbook}
}
During the past decade, brain–computer interfaces (BCIs) have rapidly developed, both in technological and application domains. However, most of these interfaces rely on the visual modality. Only some research groups have been studying non-visual BCIs, primarily based on auditory and, sometimes, on somatosensory signals. These non-visual BCI approaches are especially useful for severely disabled patients with poor vision. From a broader perspective, multisensory BCIs may offer more versatile and user-friendly paradigms for control and feedback. This chapter describes current systems that are used within auditory and somatosensory BCI research. Four categories of noninvasive BCI paradigms are employed: (1) P300 evoked potentials, (2) steady-state evoked potentials, (3) slow cortical potentials, and (4) mental tasks. Comparing visual and non-visual BCIs, we propose and discuss different possible multisensory combinations, as well as their pros and cons. We conclude by discussing potential future research directions of multisensory BCIs and related research questions
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.
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).
Daly, Ian; Nasuto, Slawomir J.; Warwick, Kevin
Brain computer interface control via functional connectivity dynamics Journal Article
In: Pattern Recognition, vol. 45, no. 6, pp. 2123–2136, 2012.
Abstract | Links | BibTeX | Tags: BCI, Complex networks, Finger tapping, Functional connectivity, HMM, Phase synchronization
@article{Daly2012a,
title = {Brain computer interface control via functional connectivity dynamics},
author = {Ian Daly and Slawomir J. Nasuto and Kevin Warwick},
url = {http://www.iandaly.co.uk/wp-content/uploads/2016/01/Functional-connectivity-during-finger-taps.pdf},
doi = {10.1016/j.patcog.2011.04.034},
year = {2012},
date = {2012-06-01},
journal = {Pattern Recognition},
volume = {45},
number = {6},
pages = {2123–2136},
abstract = {The dynamics of inter-regional communication within the brain during cognitive processing – referred to as functional connectivity – are investigated as a control feature for a brain computer interface.
EMDPL is used to map phase synchronization levels between all channel pair combinations in the EEG. This results in complex networks of channel connectivity at all time–frequency locations. The mean clustering coefficient is then used as a descriptive feature encapsulating information about inter-channel connectivity.
Hidden Markov models are applied to characterize and classify dynamics of the resulting complex networks. Highly accurate levels of classification are achieved when this technique is applied to classify EEG recorded during real and imagined single finger taps. These results are compared to traditional features used in the classification of a finger tap BCI demonstrating that functional connectivity dynamics provide additional information and improved BCI control accuracies.},
keywords = {BCI, Complex networks, Finger tapping, Functional connectivity, HMM, Phase synchronization},
pubstate = {published},
tppubtype = {article}
}
The dynamics of inter-regional communication within the brain during cognitive processing – referred to as functional connectivity – are investigated as a control feature for a brain computer interface.
EMDPL is used to map phase synchronization levels between all channel pair combinations in the EEG. This results in complex networks of channel connectivity at all time–frequency locations. The mean clustering coefficient is then used as a descriptive feature encapsulating information about inter-channel connectivity.
Hidden Markov models are applied to characterize and classify dynamics of the resulting complex networks. Highly accurate levels of classification are achieved when this technique is applied to classify EEG recorded during real and imagined single finger taps. These results are compared to traditional features used in the classification of a finger tap BCI demonstrating that functional connectivity dynamics provide additional information and improved BCI control accuracies.
EMDPL is used to map phase synchronization levels between all channel pair combinations in the EEG. This results in complex networks of channel connectivity at all time–frequency locations. The mean clustering coefficient is then used as a descriptive feature encapsulating information about inter-channel connectivity.
Hidden Markov models are applied to characterize and classify dynamics of the resulting complex networks. Highly accurate levels of classification are achieved when this technique is applied to classify EEG recorded during real and imagined single finger taps. These results are compared to traditional features used in the classification of a finger tap BCI demonstrating that functional connectivity dynamics provide additional information and improved BCI control accuracies.
Breitwieser, Christian; Daly, Ian; Neuper, Christa; Muller-Putz, Gernot R.
Proposing a Standardized Protocol for Raw Biosignal Transmission Journal Article
In: IEEE Transactions on Biomedical Engineering, vol. 59, no. 3, pp. 852-859, 2012.
Abstract | Links | BibTeX | Tags: BCI, Protocol, TiA, TOBI, Tools
@article{Breitwieser2011b,
title = {Proposing a Standardized Protocol for Raw Biosignal Transmission},
author = {Christian Breitwieser and Ian Daly and Christa Neuper and Gernot R. Muller-Putz},
url = {http://www.ncbi.nlm.nih.gov/pubmed/22194230},
doi = {10.1109/TBME.2011.2174637},
year = {2012},
date = {2012-03-01},
journal = {IEEE Transactions on Biomedical Engineering},
volume = {59},
number = {3},
pages = {852-859},
abstract = {In this paper, we propose a standardized interface called TiA (TOBI interface A) to transmit raw biosignals, supporting multirate and block-oriented transmission of different kinds of signals from various acquisition devices (e.g., EEG, electrooculogram, near-infrared spectroscopy signals, etc.) at the same time. To facilitate a distinction between those kinds of signals, so-called signal types are introduced. TiA is a single-server, multiple-client system, whereby clients can connect to the server at runtime. Information transfer between client and server is divided into control and data connections. The control connections use transmission control protocol (TCP) and transmit extensible-markup-language (XML)-encoded meta information. The data transmission utilizes a user datagram protocol (UDP) or TCP with a binary data stream. A standardized handshaking procedure for the connection setup and a standardized binary data packet has been defined. Thus, a standardized layer, abstracting used hardware devices and facilitating distributed raw data transmission in a standardized way, has been evolved. A cross-platform library, implemented in C ++, is available for download.},
keywords = {BCI, Protocol, TiA, TOBI, Tools},
pubstate = {published},
tppubtype = {article}
}
In this paper, we propose a standardized interface called TiA (TOBI interface A) to transmit raw biosignals, supporting multirate and block-oriented transmission of different kinds of signals from various acquisition devices (e.g., EEG, electrooculogram, near-infrared spectroscopy signals, etc.) at the same time. To facilitate a distinction between those kinds of signals, so-called signal types are introduced. TiA is a single-server, multiple-client system, whereby clients can connect to the server at runtime. Information transfer between client and server is divided into control and data connections. The control connections use transmission control protocol (TCP) and transmit extensible-markup-language (XML)-encoded meta information. The data transmission utilizes a user datagram protocol (UDP) or TCP with a binary data stream. A standardized handshaking procedure for the connection setup and a standardized binary data packet has been defined. Thus, a standardized layer, abstracting used hardware devices and facilitating distributed raw data transmission in a standardized way, has been evolved. A cross-platform library, implemented in C ++, is available for download.
2011
Portelli, Anthony J.; Daly, Ian; Spencer, Mathew; Nasuto, Slawomir J.
Low Cost Brain Computer Interface First Results Conference
Proceedings of the 5th International Brain-Computer Interface Conference 2011, 2011.
Abstract | Links | BibTeX | Tags: BCI, EEG, Low-cost BCI
@conference{Portelli2011,
title = {Low Cost Brain Computer Interface First Results},
author = {Anthony J. Portelli and Ian Daly and Mathew Spencer and Slawomir J. Nasuto},
url = {http://www.iandaly.co.uk/wp-content/uploads/2016/01/Low-Cost-Brain-Computer-Interface.pdf},
year = {2011},
date = {2011-09-01},
booktitle = {Proceedings of the 5th International Brain-Computer Interface Conference 2011},
abstract = {Brain Computer Interfacing (BCI) has been previously demonstrated to restore patient communication, meeting with varying degrees of success. Due to the nature of the equipment traditionally used in BCI experimentation (the electroencephalograph) it is mostly confined to clinical and research environments. The required medical safety standards, subsequent cost of equipment and its application/training times are all issues that need to be resolved if BCIs are to be taken out of the lab/clinic and delivered to the home market. The results in this paper demonstrate a system developed with a low cost medical grade EEG amplifier unit in conjunction with the open source BCI2000 software suite thus constructing the cheapest per electrode system available, meeting rigorous clinical safety standards. Discussion of the future of this technology and future work concerning this platform are also introduced.},
keywords = {BCI, EEG, Low-cost BCI},
pubstate = {published},
tppubtype = {conference}
}
Brain Computer Interfacing (BCI) has been previously demonstrated to restore patient communication, meeting with varying degrees of success. Due to the nature of the equipment traditionally used in BCI experimentation (the electroencephalograph) it is mostly confined to clinical and research environments. The required medical safety standards, subsequent cost of equipment and its application/training times are all issues that need to be resolved if BCIs are to be taken out of the lab/clinic and delivered to the home market. The results in this paper demonstrate a system developed with a low cost medical grade EEG amplifier unit in conjunction with the open source BCI2000 software suite thus constructing the cheapest per electrode system available, meeting rigorous clinical safety standards. Discussion of the future of this technology and future work concerning this platform are also introduced.
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.
Daly, Ian; Nasuto, Slawomir J.; Warwick, Kevin
Single tap identification for fast BCI control Journal Article
In: Cognitive Neurodynamics, vol. 5, no. 1, pp. 21-30, 2011, ISSN: 1871-4080.
Abstract | Links | BibTeX | Tags: BCI, DE, ERD, Features selection, Finger tapping, Single trial
@article{Daly2010,
title = {Single tap identification for fast BCI control},
author = {Ian Daly and Slawomir J. Nasuto and Kevin Warwick},
url = {http://www.iandaly.co.uk/wp-content/uploads/2016/01/Single-tap-identification-for-fast-BCI-control.pdf},
doi = {10.1007/s11571-010-9133-x},
issn = {1871-4080},
year = {2011},
date = {2011-03-01},
journal = {Cognitive Neurodynamics},
volume = {5},
number = {1},
pages = {21-30},
abstract = {One of the major aims of BCI research is devoted to achieving faster and more efficient control of external devices. The identification of individual tap events in a motor imagery BCI is therefore a desirable goal. EEG is recorded from subjects performing and imagining finger taps with their left and right hands. A Differential Evolution based feature selection wrapper is used in order to identify optimal features in the spatial and frequency domains for tap identification. Channel-frequency band combinations are found which allow differentiation of tap vs. no-tap control conditions for executed and imagined taps. Left vs. right hand taps may also be differentiated with features found in this manner. A sliding time window is then used to accurately identify individual taps in the executed tap and imagined tap conditions. Highly statistically significant classification accuracies are achieved with time windows of 0.5 s and more allowing taps to be identified on a single trial basis.},
keywords = {BCI, DE, ERD, Features selection, Finger tapping, Single trial},
pubstate = {published},
tppubtype = {article}
}
One of the major aims of BCI research is devoted to achieving faster and more efficient control of external devices. The identification of individual tap events in a motor imagery BCI is therefore a desirable goal. EEG is recorded from subjects performing and imagining finger taps with their left and right hands. A Differential Evolution based feature selection wrapper is used in order to identify optimal features in the spatial and frequency domains for tap identification. Channel-frequency band combinations are found which allow differentiation of tap vs. no-tap control conditions for executed and imagined taps. Left vs. right hand taps may also be differentiated with features found in this manner. A sliding time window is then used to accurately identify individual taps in the executed tap and imagined tap conditions. Highly statistically significant classification accuracies are achieved with time windows of 0.5 s and more allowing taps to be identified on a single trial basis.
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.
2008
Daly, Ian; Nasuto, Slawomir J.; Warwick, Kevin
Towards natural human computer interaction in BCI Conference
Proceedings of the International symposium on artificial intelligence and simulated behaviour, Aberdeen, UK, AISB 2008.
Abstract | Links | BibTeX | Tags: BCI, Classification, EEG, Speech
@conference{IanDalySlawomirJ.Nasuto2008,
title = {Towards natural human computer interaction in BCI},
author = {Ian Daly and Slawomir J. Nasuto and Kevin Warwick},
url = {http://www.iandaly.co.uk/wp-content/uploads/2016/01/Towards-natural-human-computer-interaction-in-BCI.pdf},
year = {2008},
date = {2008-09-01},
booktitle = {Proceedings of the International symposium on artificial intelligence and simulated behaviour, Aberdeen, UK},
organization = {AISB},
abstract = {BCI systems require correct classification of signals interpreted from the brain for useful operation. To this end this paper investigates a method proposed in [1] to correctly classify a series of images presented to a group of subjects in [2].
We show that it is possible to use the proposed methods to correctly recognise the original stimuli presented to a subject from analysis of their EEG. Additionally we use a verification set to show that the trained classification method can be applied to a different set of data.
We go on to investigate the issue of invariance in EEG signals. That is, the brain representation of similar stimuli is recognisable across different subjects.
Finally we consider the usefulness of the methods investigated towards an improved BCI system and discuss how it could potentially lead to great improvements in the ease of use for the end user by offering an alternative, more intuitive control based mode of operation.},
keywords = {BCI, Classification, EEG, Speech},
pubstate = {published},
tppubtype = {conference}
}
BCI systems require correct classification of signals interpreted from the brain for useful operation. To this end this paper investigates a method proposed in [1] to correctly classify a series of images presented to a group of subjects in [2].
We show that it is possible to use the proposed methods to correctly recognise the original stimuli presented to a subject from analysis of their EEG. Additionally we use a verification set to show that the trained classification method can be applied to a different set of data.
We go on to investigate the issue of invariance in EEG signals. That is, the brain representation of similar stimuli is recognisable across different subjects.
Finally we consider the usefulness of the methods investigated towards an improved BCI system and discuss how it could potentially lead to great improvements in the ease of use for the end user by offering an alternative, more intuitive control based mode of operation.
We show that it is possible to use the proposed methods to correctly recognise the original stimuli presented to a subject from analysis of their EEG. Additionally we use a verification set to show that the trained classification method can be applied to a different set of data.
We go on to investigate the issue of invariance in EEG signals. That is, the brain representation of similar stimuli is recognisable across different subjects.
Finally we consider the usefulness of the methods investigated towards an improved BCI system and discuss how it could potentially lead to great improvements in the ease of use for the end user by offering an alternative, more intuitive control based mode of operation.