@article{Li2021,

title = {Enhancing P300 based character recognition performance using a combination of ensemble classifiers and a fuzzy fusion method},

author = {Shurui Li and Ian Daly and Xingyu Wang and Hak-Keung Lam and Andrzej Cichocki},

year  = {2021},

date = {2021-08-05},

journal = {Journal of Neuroscience Methods},

keywords = {BCI, Classification, EEG, ERP, Event-related potential, Fuzzy logic, P300},

pubstate = {published},

tppubtype = {article}

}

@article{Chen2020,

title = {The Effects of Visual Attention on Tactile P300 BCI},

author = {Zongmei Chen and Jing Jin and Ian Daly and Cili Zuo and Xingyu Wang and Andrzej Cichocki},

year  = {2020},

date = {2020-02-01},

journal = {Computational Intelligence and Neuroscience},

keywords = {BCI, P300, Tactile BCI},

pubstate = {published},

tppubtype = {article}

}

@article{Jin2019c,

title = {The Study of Generic Model Set for Reducing Calibration Time in P300-based Brain-Computer Interface},

author = {Jing Jin and Shurui Li and Ian Daly and Yangyang Miao and Chang Liu and Xingyu Wang and Andrzej Cichocki},

url = {https://ieeexplore.ieee.org/document/8917686},

doi = {10.1109/TNSRE.2019.2956488},

year  = {2019},

date = {2019-11-28},

journal = {IEEE Transactions on Neural Systems and Rehabilitation Engineering},

abstract = {P300-based brain-computer interfaces (BCIs) provide an additional communication channel for individuals with communication disabilities. In general, P300-based BCIs need to be trained, offline, for a considerable period of time, which causes users to become fatigued. This reduces the efficiency and performance of the system. In order to shorten calibration time and improve system performance, we introduce the concept of a generic model set. We used ERP data from 116 participants to train the generic model set. The resulting set consists of ten models, which are trained by weighted linear discriminant analysis (WLDA). Twelve new participants were then invited to test the validity of the generic model set. The results demonstrated that all new participants matched the best generic model. The resulting mean classification accuracy equaled 80% after online training, an accuracy that was broadly equivalent to the typical training model method. Moreover, the calibration time was shortened by 70.7% of the calibration time of the typical model method. In other words, the best matching model method only took 81s to calibrate, while the typical model method took 276s. There were also significant differences in both accuracy and raw bit rate between the best and the worst matching model methods. We conclude that the strategy of combining the generic models with online training is easily accepted and achieves higher levels of user satisfaction (as measured by subjective reports). Thus, we provide a valuable new strategy for improving the performance of P300-based BCI.},

keywords = {BCI, ERP, Event-related potential, Machine learning, P300},

pubstate = {published},

tppubtype = {article}

}

@article{Cheng2018,

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},

doi = {10.1515/bmt-2017-0082},

year  = {2018},

date = {2018-02-13},

journal = {Biomed Tech (Berl)},

keywords = {BCI, ERP, P300},

pubstate = {published},

tppubtype = {article}

}

@article{JingDaly2017a,

title = {An improved P300 pattern in BCI to catch user’s attention},

author = {Jing Jin and Hanhan Zhang and Ian Daly and Xingyu Wang and Andrezej Chiciocki},

year  = {2017},

date = {2017-02-24},

journal = {Journal of Neural Engineering},

keywords = {BCI, P300},

pubstate = {published},

tppubtype = {article}

}

@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}

}