@article{Liang2022,

title = {Novel channel selection model based on graph convolutional network for motor imagery},

author = {Wei Liang and Jing Jin and Ian Daly and Hao Sun and Xingyu Wang and Andrzej Cichocki },

doi = {https://doi.org/10.1007/s11571-022-09892-1},

year  = {2022},

date = {2022-10-10},

urldate = {2022-10-10},

journal = {Cognitive Neurodynamics},

keywords = {Channel selection, EEG, Event related (de)/synchronisation, Machine learning, Motor imagery},

pubstate = {published},

tppubtype = {article}

}

@article{Liu2022,

title = {Bispectrum-based hybrid neural network for motor imagery classification},

author = {Chang Liua and Jing Jin and Ian Daly and Hao Sun and Yitao Huang and Xingyu Wang and AndrzejCichocki},

doi = {https://doi.org/10.1016/j.jneumeth.2022.109593},

year  = {2022},

date = {2022-04-06},

urldate = {2022-04-06},

journal = {Journal of Neuroscience Methods},

volume = {375},

keywords = {Classification, Machine learning, Motor imagery},

pubstate = {published},

tppubtype = {article}

}

@article{Rybar2022,

title = {Neural decoding of semantic concepts: A systematic literature review},

author = {Milan Rybar, Ian Daly},

year  = {2022},

date = {2022-03-23},

journal = {Journal of Neural Engineering},

keywords = {BCI, Machine learning, Review, Semantic BCI, Semantic decoding, Systematic review},

pubstate = {published},

tppubtype = {article}

}

@article{Liu2022-sincNet,

title = {SincNet-Based Hybrid Neural Network for Motor Imagery EEG Decoding},

author = {Chang Liu and Jing Jin and Ian Daly and Shurui Li and Hao Sun and Yitao Huang and Xingyu Wang and Andrej Cichocki},

doi = {10.1109/TNSRE.2022.3156076},

year  = {2022},

date = {2022-03-02},

urldate = {2022-03-02},

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

volume = {30},

keywords = {Classification, Machine learning, Motor imagery},

pubstate = {published},

tppubtype = {article}

}

@article{Fang2022,

title = {Feature Extraction Method Based on Filter Banks and Riemannian Tangent Space in Motor-Imagery BCI},

author = {Hua Fang and Jing Jin and Ian Daly and Xing yu Wang},

doi = {10.1109/JBHI.2022.3146274},

year  = {2022},

date = {2022-01-31},

journal = {IEEE Journal of Biomedical and Health Informatics},

keywords = {BCI, EEG, Feature selection, Machine learning},

pubstate = {published},

tppubtype = {article}

}

@article{Jin2021optMod,

title = {Optimization of Model Training Based on Iterative Minimum Covariance Determinant in Motor-Imagery BCI},

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

year  = {2021},

date = {2021-04-18},

journal = {International Journal of Neural Systems},

keywords = {BCI, Classification, EEG, ERD, Machine learning, Motor imagery},

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{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{Jin2019NN,

title = {Correlation-based channel selection and regularized feature optimization for MI-based BCI},

author = {Jing Jin and Yangyang Miao and Ian Daly and Cili Zuo and Dewen Hu and Andrzej Cichocki},

url = {https://www.sciencedirect.com/science/article/pii/S0893608019301960?dgcid=coauthor},

doi = {https://doi.org/10.1016/j.neunet.2019.07.008},

year  = {2019},

date = {2019-07-15},

journal = {Neural Networks},

keywords = {BCI, Channel selection, EEG, Feature selection, Machine learning, Motor imagery},

pubstate = {published},

tppubtype = {article}

}

@article{Feng2019,

title = {An Optimized Channel Selection Method based on Multi-frequency CSP-rank for Motor Imagery-based BCI system},

author = {Jiankui Feng and Jing Jin and Ian Daly and Jiale Zhou and Yugang Niu and Xingyu Wang and Andrzej Cichocki},

year  = {2019},

date = {2019-04-18},

journal = {Computational Intelligence and Neuroscience},

keywords = {BCI, Feature selection, Machine learning, Motor imagery},

pubstate = {published},

tppubtype = {article}

}

@article{Feng2018,

title = {Towards correlation-based time window selection method for motor imagery BCIs},

author = {Jiankui Feng and Erwei Yin and Jing Jin and Rami Saab and Ian Daly and Xingyu Wang and Dewen Hu and Andrzej Cichocki},

doi = {https://doi.org/10.1016/j.neunet.2018.02.011},

year  = {2018},

date = {2018-06-01},

journal = {Neural Networks},

volume = {102},

pages = {87-95},

abstract = {The start of the cue is often used to initiate the feature window used to control motor imagery (MI)-based brain-computer interface (BCI) systems. However, the time latency during an MI period varies between trials for each participant. Fixing the starting time point of MI features can lead to decreased system performance in MI-based BCI systems. To address this issue, we propose a novel correlation-based time window selection (CTWS) algorithm for MI-based BCIs. Specifically, the optimized reference signals for each class were selected based on correlation analysis and performance evaluation. Furthermore, the starting points of time windows for both training and testing samples were adjusted using correlation analysis. Finally, the feature extraction and classification algorithms were used to calculate the classification accuracy. With two datasets, the results demonstrate that the CTWS algorithm significantly improved the system performance when compared to directly using feature extraction approaches. Importantly, the average improvement in accuracy of the CTWS algorithm on the datasets of healthy participants and stroke patients was 16.72% and 5.24%, respectively when compared to traditional common spatial pattern (CSP) algorithm. In addition, the average accuracy increased 7.36% and 9.29%, respectively when the CTWS was used in conjunction with Sub-Alpha-Beta Log-Det Divergences (Sub-ABLD) algorithm. These findings suggest that the proposed CTWS algorithm holds promise as a general feature extraction approach for MI-based BCIs.},

keywords = {BCI, Machine learning, Motor imagery},

pubstate = {published},

tppubtype = {article}

}

@article{Daly2017b,

title = {Personalised, Multi-modal, Affective State Detection for Hybrid Brain-Computer Music Interfacing},

author = {Ian Daly and Duncan Williams and Asad Malik and James Weaver and Alexis Kirke and Faustina Hwang and Eduardo Miranda and Slawomir J. Nasuto},

year  = {2017},

date = {2017-10-08},

journal = {IEEE Transactions on Affective Computing},

abstract = {Brain-computer music interfaces (BCMIs) may be used to modulate affective states, with applications in music therapy, composition, and entertainment. However, for such systems to work they need to be able to reliably detect their user’s current affective state.

We present a method for personalised affective state detection for use in BCMI. We compare it to a population-based detection method trained on 17 users and demonstrate that personalised affective state detection is significantly (p < 0:01) more accurate, with average improvements in accuracy of 10.2% for valence and 9.3% for arousal. We also compare a hybrid BCMI (a BCMI that combines physiological signals with neurological signals) to a conventional BCMI design 

 one based upon the use of only EEG features) and demonstrate that the hybrid design results in a significant (p < 0:01) 6.2% improvement in performance for arousal classification and a significant (p < 0:01) 5.9% improvement for valence classification.},

keywords = {Affective computing, BCI, Classification, Feature selection, Machine learning},

pubstate = {published},

tppubtype = {article}

}