Publications
2016
Daly, Ian; Williams, Duncan; Kirke, Alexis; Weaver, James; Malik, Asad; Hwang, Faustina; Miranda, Eduardo; Nasuto, Slawomir J.
Affective Brain-Computer Music Interfacing Journal Article
In: Journal of Neural Engineering, vol. (accepted), 2016.
BibTeX | Tags: aBCMI, Affective composition, BCI, BCMI, Case based reasoning, EEG, Emotion, Hybrid BCI, Music generation
@article{Daly2016aBCMI,
title = {Affective Brain-Computer Music Interfacing},
author = {Ian Daly and Duncan Williams and Alexis Kirke and James Weaver and Asad Malik and Faustina Hwang and Eduardo Miranda and Slawomir J. Nasuto},
year = {2016},
date = {2016-06-21},
journal = {Journal of Neural Engineering},
volume = {(accepted)},
keywords = {aBCMI, Affective composition, BCI, BCMI, Case based reasoning, EEG, Emotion, Hybrid BCI, Music generation},
pubstate = {published},
tppubtype = {article}
}
2015
Jin, Jing; Daly, Ian; Zhang, Yu; Wang, Xingyu; Cichocki, Andrzej
A new hybrid BCI paradigm based on P300 and SSVEP Journal Article
In: Journal of Neural Engineering, vol. 244, pp. 16–25, 2015.
Abstract | Links | BibTeX | Tags: BCI, Event-related potential, Hybrid BCI, P300, SSVEP
@article{Wang2014,
title = {A new hybrid BCI paradigm based on P300 and SSVEP},
author = {Jing Jin and Ian Daly and Yu Zhang and Xingyu Wang and Andrzej Cichocki},
url = {http://www.sciencedirect.com/science/article/pii/S016502701400209X},
doi = {doi:10.1016/j.jneumeth.2014.06.003},
year = {2015},
date = {2015-04-15},
journal = {Journal of Neural Engineering},
volume = {244},
pages = {16–25},
abstract = {Background
P300 and steady-state visual evoked potential (SSVEP) approaches have been widely used for brain–computer interface (BCI) systems. However, neither of these approaches can work for all subjects. Some groups have reported that a hybrid BCI that combines two or more approaches might provide BCI functionality to more users. Hybrid P300/SSVEP BCIs have only recently been developed and validated, and very few avenues to improve performance have been explored.
New method
The present study compares an established hybrid P300/SSVEP BCIs paradigm to a new paradigm in which shape changing, instead of color changing, is adopted for P300 evocation to decrease the degradation on SSVEP strength.
Result
The result shows that the new hybrid paradigm presented in this paper yields much better performance than the normal hybrid paradigm.
Comparison with existing method
A performance increase of nearly 20% in SSVEP classification is achieved using the new hybrid paradigm in comparison with the normal hybrid paradigm. All the paradigms except the normal hybrid paradigm used in this paper obtain 100% accuracy in P300 classification.
Conclusions
The new hybrid P300/SSVEP BCIs paradigm in which shape changing, instead of color changing, could obtain as high classification accuracy of SSVEP as the traditional SSVEP paradigm and could obtain as high classification accuracy of P300 as the traditional P300 paradigm. P300 did not interfere with the SSVEP response using the new hybrid paradigm presented in this paper, which was superior to the normal hybrid P300/SSVEP paradigm.},
keywords = {BCI, Event-related potential, Hybrid BCI, P300, SSVEP},
pubstate = {published},
tppubtype = {article}
}
Background
P300 and steady-state visual evoked potential (SSVEP) approaches have been widely used for brain–computer interface (BCI) systems. However, neither of these approaches can work for all subjects. Some groups have reported that a hybrid BCI that combines two or more approaches might provide BCI functionality to more users. Hybrid P300/SSVEP BCIs have only recently been developed and validated, and very few avenues to improve performance have been explored.
New method
The present study compares an established hybrid P300/SSVEP BCIs paradigm to a new paradigm in which shape changing, instead of color changing, is adopted for P300 evocation to decrease the degradation on SSVEP strength.
Result
The result shows that the new hybrid paradigm presented in this paper yields much better performance than the normal hybrid paradigm.
Comparison with existing method
A performance increase of nearly 20% in SSVEP classification is achieved using the new hybrid paradigm in comparison with the normal hybrid paradigm. All the paradigms except the normal hybrid paradigm used in this paper obtain 100% accuracy in P300 classification.
Conclusions
The new hybrid P300/SSVEP BCIs paradigm in which shape changing, instead of color changing, could obtain as high classification accuracy of SSVEP as the traditional SSVEP paradigm and could obtain as high classification accuracy of P300 as the traditional P300 paradigm. P300 did not interfere with the SSVEP response using the new hybrid paradigm presented in this paper, which was superior to the normal hybrid P300/SSVEP paradigm.
P300 and steady-state visual evoked potential (SSVEP) approaches have been widely used for brain–computer interface (BCI) systems. However, neither of these approaches can work for all subjects. Some groups have reported that a hybrid BCI that combines two or more approaches might provide BCI functionality to more users. Hybrid P300/SSVEP BCIs have only recently been developed and validated, and very few avenues to improve performance have been explored.
New method
The present study compares an established hybrid P300/SSVEP BCIs paradigm to a new paradigm in which shape changing, instead of color changing, is adopted for P300 evocation to decrease the degradation on SSVEP strength.
Result
The result shows that the new hybrid paradigm presented in this paper yields much better performance than the normal hybrid paradigm.
Comparison with existing method
A performance increase of nearly 20% in SSVEP classification is achieved using the new hybrid paradigm in comparison with the normal hybrid paradigm. All the paradigms except the normal hybrid paradigm used in this paper obtain 100% accuracy in P300 classification.
Conclusions
The new hybrid P300/SSVEP BCIs paradigm in which shape changing, instead of color changing, could obtain as high classification accuracy of SSVEP as the traditional SSVEP paradigm and could obtain as high classification accuracy of P300 as the traditional P300 paradigm. P300 did not interfere with the SSVEP response using the new hybrid paradigm presented in this paper, which was superior to the normal hybrid P300/SSVEP paradigm.
2013
Daly, Ian; Aloise, Fabio; Arico, Pietro; Belda, Juan; Billinger, Martin; Bolinger, Elizabeth; Cincotti, Febo; Hettich, Dirk; Iosa, Marco; Laparra-Hernandez, Jose; Scherer, Reinhold; Müller-Putz, Gernot
Rapid prototyping for hBCI users with Cerebral palsy Conference
Proceedings of BCI meeting 2013, 2013.
BibTeX | Tags: Cerebral palsy, ERD, Hybrid BCI, Tools
@conference{Daly2013BCImtg,
title = {Rapid prototyping for hBCI users with Cerebral palsy},
author = {Ian Daly and Fabio Aloise and Pietro Arico and Juan Belda and Martin Billinger and Elizabeth Bolinger and Febo Cincotti and Dirk Hettich and Marco Iosa and Jose Laparra-Hernandez and Reinhold Scherer and Gernot Müller-Putz},
year = {2013},
date = {2013-09-01},
booktitle = {Proceedings of BCI meeting 2013},
keywords = {Cerebral palsy, ERD, Hybrid BCI, Tools},
pubstate = {published},
tppubtype = {conference}
}
2012
Daly, Ian; Grissmann, Sebastian; Brunner, Clemens; Allison, Brendan Z.; Müller-Putz, Gernot
Hybrid BCI classification via dynamic re-weighting Conference
Proceedings of the 3rd TOBI workshop, Würzburg, Germany, 2012.
Abstract | BibTeX | Tags: Classifier, EEG, ERD, Hybrid BCI, SSVEP
@conference{Daly2012,
title = {Hybrid BCI classification via dynamic re-weighting},
author = {Ian Daly and Sebastian Grissmann and Clemens Brunner and Brendan Z. Allison and Gernot Müller-Putz},
year = {2012},
date = {2012-10-01},
booktitle = {Proceedings of the 3rd TOBI workshop, Würzburg, Germany},
abstract = {A hybrid brain-computer interface (hBCI) may combine two or more BCI paradigms with the objective of improving performance (accuracy, stability, bit rate etc.) over that achievable with a single paradigm. However, the approach taken in some recent hBCI studies did not achieve accuracies significantly better than a single paradigm. Therefore, we introduce a re-weighting method for classifying a hybrid feature set. This approach produces higher accuracies than with the ERD paradigm.},
keywords = {Classifier, EEG, ERD, Hybrid BCI, SSVEP},
pubstate = {published},
tppubtype = {conference}
}
A hybrid brain-computer interface (hBCI) may combine two or more BCI paradigms with the objective of improving performance (accuracy, stability, bit rate etc.) over that achievable with a single paradigm. However, the approach taken in some recent hBCI studies did not achieve accuracies significantly better than a single paradigm. Therefore, we introduce a re-weighting method for classifying a hybrid feature set. This approach produces higher accuracies than with the ERD paradigm.