@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{Ramirez-Torres2021,

title = {How to build a fast and accurate Code-Modulated Brain-Computer Interface},

author = {Juan Ramirez Torres and Ian Daly},

year  = {2021},

date = {2021-04-21},

journal = {Journal of Neural Engineering},

keywords = {BCI, Classification, cVEP, EEG, ERP, Event-related potential, Feature selection},

pubstate = {published},

tppubtype = {article}

}

@article{Daly2020NCA,

title = {Neural component analysis: a spatial filter for electroencephalogram analysis},

author = {Ian Daly},

year  = {2020},

date = {2020-10-20},

journal = {Journal of Neuroscience Methods},

keywords = {Classification, EEG, ERP, Event-related potential, Feature selection, Machine learning},

pubstate = {published},

tppubtype = {article}

}

@article{Li2020,

title = {Comparison of the ERP-Based BCI Performance Among Chromatic (RGB) Semitransparent Face Patterns},

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

doi = {10.3389/fnins.2020.00054},

year  = {2020},

date = {2020-01-31},

journal = {Frontiers Neuroscience},

volume = {14},

number = {54},

pages = {12},

abstract = {Objective: Previous studies have shown that combing with color properties may be

used as part of the display presented to BCI users in order to improve performance.

Build on this, we explored the effects of combinations of face stimuli with three

primary colors (RGB) on BCI performance which is assessed by classification accuracy

and information transfer rate (ITR). Furthermore, we analyzed the waveforms of

three patterns.



Methods: We compared three patterns in which semitransparent face is overlaid three

primary colors as stimuli: red semitransparent face (RSF), green semitransparent face

(GSF), and blue semitransparent face (BSF). Bayesian linear discriminant analysis (BLDA)

was used to construct the individual classifier model. In addition, a Repeated-measures

ANOVA (RM-ANOVA) and Bonferroni correction were chosen for statistical analysis.



Results: The results indicated that the RSF pattern achieved the highest online

averaged accuracy with 93.89%, followed by the GSF pattern with 87.78%, while the

lowest performance was caused by the BSF pattern with an accuracy of 81.39%.

Furthermore, significant differences in classification accuracy and ITR were found

between RSF and GSF .p < 0:05/ and between RSF and BSF patterns .p < 0:05/.



Conclusion: The semitransparent faces colored red (RSF) pattern yielded the best

performance of the three patterns. The proposed patterns based on ERP-BCI system

have a clinically significant impact by increasing communication speed and accuracy of

the P300-speller for patients with severe motor impairment.},

keywords = {BCI, ERP, Event-related potential},

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{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}

}

@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}

}