@article{nokey,

title = {Classification of motor imagery based on multi-scale feature extraction and the channel-temporal attention module},

author = {Runze Wu and Jing Jin and Ian Daly and Xingyu Wang and Andrzej Cichocki},

url = {https://ieeexplore.ieee.org/document/10180110},

doi = {10.1109/TNSRE.2023.3294815},

year  = {2023},

date = {2023-07-11},

urldate = {2023-07-11},

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

keywords = {BCI, Classification, EEG, Event related (de)/synchronisation, 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{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{Jin2021Graph,

title = {A Novel Classification Framework Using the Graph Representations of Electroencephalogram for Motor Imagery based Brain-Computer Interface},

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

doi = {10.1109/TNSRE.2021.3139095},

year  = {2021},

date = {2021-12-28},

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

abstract = {The motor imagery (MI) based brain-computer interfaces (BCIs) have been proposed as a potential physical rehabilitation technology. However, the low classification accuracy achievable with MI tasks is still a challenge when building effective BCI systems. We propose a novel MI classification model based on measurement of functional connectivity between brain regions and graph theory. Specifically, motifs describing local network structures in the brain are extracted from functional connectivity graphs. A graph embedding model called Ego-CNNs is then used to build a classifier, which can convert the graph from a structural representation to a fixed-dimensional vector for detecting critical structure in the graph. We validate our proposed method on four datasets, and the results show that our proposed method produces high classification accuracies in two-class classification tasks (92.8% for dataset 1, 93.4% for dataset 2, 96.5% for dataset 3, and 80.2% for dataset 4) and multiclass classification tasks (90.33% for dataset 1). Our proposed method achieves a mean Kappa value of 0.88 across nine participants, which is superior to other methods we compared it to. These results indicate that there is a local structural difference in functional connectivity graphs extracted under different motor imagery tasks. Our proposed method has great potential for motor imagery classification in future studies.},

keywords = {BCI, Classification, EEG, ERD, Functional connectivity, Motor imagery},

pubstate = {published},

tppubtype = {article}

}

@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{Daly2021Art,

title = {Removal of physiological artifacts from simultaneous EEG and fMRI recordings},

author = {Ian Daly},

year  = {2021},

date = {2021-06-01},

journal = {Clinical Neurophysiology},

keywords = {Affective computing, Artefact removal, Classification, EEG, fMRI},

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{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{Jin2020bispectrum,

title = {Bispectrum-based Channel Selection for Motor Imagery based Brain-Computer Interfacing},

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

doi = {10.1109/TNSRE.2020.3020975},

year  = {2020},

date = {2020-09-01},

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

keywords = {BCI, Classification, Feature selection, 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}

}

@article{Jin2012,

title = {Whether generic model works for rapid ERP-based BCI calibration},

author = {Jing Jin and Eric W. Sellers and Yu Zhang and Ian Daly and Xingyu Wang and Andrzej Cichocki},

doi = {10.1016/j.jneumeth.2012.09.020},

year  = {2013},

date = {2013-01-13},

journal = {Journal of Neuroscience Methods},

volume = {212},

number = {1},

pages = {94-99},

abstract = {Event-related potential (ERP)-based brain-computer interfacing (BCI) is an effective method of basic communication. However, collecting calibration data, and classifier training, detracts from the amount of time allocated for online communication. Decreasing calibration time can reduce preparation time thereby allowing for additional online use, potentially lower fatigue, and improved performance. Previous studies, using generic online training models which avoid offline calibration, afford more time for online spelling. Such studies have not examined the direct effects of the model on individual performance, and the training sequence exceeded the time reported here. The first goal of this work is to survey whether one generic model works for all subjects and the second goal is to show the performance of a generic model using an online training strategy when participants could use the generic model. The generic model was derived from 10 participant's data. An additional 11 participants were recruited for the current study. Seven of the participants were able to use the generic model during online training. Moreover, the generic model performed as well as models obtained from participant specific offline data with a mean training time of less than 2 min. However, four of the participants could not use this generic model, which shows that one generic mode is not generic for all subjects. More research on ERPs of subjects with different characteristics should be done, which would be helpful to build generic models for subject groups. This result shows a potential valuable direction for improving the BCI system.},

keywords = {BCI, Classification, Event-related potentials, Generic model},

pubstate = {published},

tppubtype = {article}

}