@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{Miao2021,

title = {Learning Common Time-Frequency-Spatial Patterns for Motor Imagery Classification},

author = {Yangyang Miao and Jing Jin and Ian Daly and Cili Zuo and Xingyu Wang and Andrzej Cichocki and Tzyy-Ping Jung},

doi = {10.1109/TNSRE.2021.3071140},

year  = {2021},

date = {2021-04-05},

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

abstract = {The common spatial patterns (CSP) algorithm is the most popular spatial filtering method applied to extract electroencephalogram (EEG) features for motor imagery (MI) based brain-computer interface (BCI) systems. The effectiveness of the CSP algorithm depends on optimal selection of the frequency band and time window from the EEG. Many algorithms have been designed to optimize frequency band selection for CSP, while few algorithms seek to optimize the time window. This study proposes a novel framework, termed common time-frequency-spatial patterns (CTFSP), to extract sparse CSP features from multi-band filtered EEG data in multiple time windows. Specifically, the whole MI period is first segmented into multiple subseries using a sliding time window approach. Then, sparse CSP features are extracted from multiple frequency bands in each time window. Finally, multiple support vector machine (SVM) classifiers with the Radial Basis Function (RBF) kernel are trained to identify the MI tasks and the voting result of these classifiers determines the final output of the BCI. This study applies the proposed CTFSP algorithm to three public EEG datasets (BCI competition III dataset IVa, BCI competition III dataset IIIa, and BCI competition IV dataset 1) to validate its effectiveness, compared against several other state-of-the-art methods. The experimental results demonstrate that the proposed algorithm is a promising candidate for improving the performance of MI-BCI systems.},

keywords = {BCI, Classification, EEG, ERD, Event-related potential, Machine learning, Motor imagery},

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{Jing2020CSPDempster-Shafer,

title = {Internal Feature Selection Method of CSP Based on L1-Norm and Dempster-Shafer Theory},

author = {Jing Jin and Ruocheng Xiao and Ian Daly and Yangyang Miao and Xingyu Wang and Andrzej Cichocki},

year  = {2020},

date = {2020-08-05},

journal = {IEEE Transactions on Neural Networks and Learning Systems},

keywords = {BCI, EEG, Event-related potential, 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{Daly2016faces,

title = {An Investigation Into The Use Of Six Facially Encoded Emotions In Brain-Computer Interfacing},

author = {Ian Daly and Long Chen and Sijie Zhou and Jing Jin},

year  = {2016},

date = {2016-02-01},

journal = {Brain Computer Interfaces},

keywords = {BCI, Emotion, Event-related potential, Facially encoded emotion, Oddball paradigm},

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}

}