@article{Tates2025,

title = {Speech Imagery Brain-Computer Interfaces: A Systematic Literature Review},

author = {Alberto Tates and Ana Matran-Fernandez and Sebastian Halder and and Ian Daly},

year  = {2025},

date = {2025-06-10},

urldate = {2025-06-10},

journal = {Journal of Neural Engineering},

keywords = {literature review, Speech BCI},

pubstate = {published},

tppubtype = {article}

}

@article{Rybar2025,

title = {Simultaneous EEG and fNIRS recordings for semantic decoding of imagined animals and tools},

author = {Milan Rybar and Riccardo Poli and Ian Daly},

year  = {2025},

date = {2025-04-12},

journal = {Journal of Neural Engineering},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Rybar2024b,

title = {Using data from cue presentations results in grossly overestimating semantic BCI performance},

author = {Milan Rybar and Riccardo Poli and Ian Daly},

year  = {2024},

date = {2024-11-07},

urldate = {2024-11-07},

journal = {Scientific Reports},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Jin2024BubbleSpeller,

title = {A Growing Bubble Speller Paradigm for Brain-Computer Interface Based on Event-related Potentials},

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

doi = { 10.1109/TBME.2024.3492506},

year  = {2024},

date = {2024-11-04},

urldate = {2024-11-04},

journal = {IEEE Transactions on Biomedical Engineering},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Jin2024Squeeze,

title = {Squeeze and Excitation-Based Multiscale CNN for Classification of Steady-State Visual Evoked Potentials},

author = {Jing Jin and Xiao Wu and Ian Daly and Weijie Chen and Xinjie He and Xingyu Wang},

url = {https://ieeexplore.ieee.org/abstract/document/10740041},

doi = {10.1109/JIOT.2024.3488745},

year  = {2024},

date = {2024-10-31},

urldate = {2024-10-31},

journal = {IEEE Internet of Things Journal },

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Li2024P300,

title = {Inter-participant transfer learning with attention based domain adversarial training for P300 detection},

author = {Shurui Li and Ian Daly and Cuntai Guan and Andrzej Cichocki and Jing Jin},

url = {https://www.sciencedirect.com/science/article/pii/S0893608024005793},

doi = {https://doi.org/10.1016/j.neunet.2024.106655},

year  = {2024},

date = {2024-08-22},

urldate = {2024-08-22},

journal = {Neural Networks},

volume = {180},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Jin2024a,

title = {MOCNN: A Multi-scale Deep Convolutional Neural Network for ERP-based Brain-Computer Interfaces},

author = {Jing Jin and Ruitian Xu and Ian Daly and Xueqing Zhao and Xingyu Wang and Andrzej Cichocki},

year  = {2024},

date = {2024-04-15},

journal = {IEEE Transactions on Cybernetics},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Sun2023,

title = {Feature learning framework based on EEG graph self-attention networks for motor imagery BCI systems},

author = {Hao Sun and Jing Jin and Ian Daly and Yitao Huang and Xueqing Zhao and Xingyu Wang and Andrzej Cichocki},

doi = {https://doi.org/10.1016/j.jneumeth.2023.109969},

year  = {2023},

date = {2023-09-03},

journal = {Journal of Neuroscience Methods},

volume = {399},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@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{Daly2022-musicDecoding,

title = {Neural decoding of music from the EEG},

author = {Ian Daly},

doi = {10.1038/s41598-022-27361-x},

year  = {2023},

date = {2023-01-12},

urldate = {2022-12-30},

journal = {Scientific Reports},

type = {Journal article},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@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{Al-Taie2022,

title = {Sonic Enhancement of Virtual Exhibits},

author = {Inas Al-Taie and Paola Di Giuseppantonio Di Franco and Michael Tymkiw and Duncan Williams and Ian Daly},

year  = {2022},

date = {2022-08-11},

journal = {PLoS One},

keywords = {Affective computing, Art, Music generation, Soundscaping},

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{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{Li2021Menger,

title = {Feature Selection method based on Menger Curvature and LDA Theory for a P300 Brain-computer Interface},

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

year  = {2021},

date = {2021-11-29},

journal = {Journal of Neural Engineering},

keywords = {BCI, EEG, Feature selection},

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{Rybar2021,

title = {Decoding of semantic categories of imagined concepts of animals and tools in fNIRS},

author = {Milan Rybar and Riccardo Poli and Ian Daly},

year  = {2021},

date = {2021-03-08},

journal = {Journal of Neural Engineering},

keywords = {BCI, fNIRS, Semantic decoding},

pubstate = {published},

tppubtype = {article}

}

@article{Miao2020,

title = {BCI-Based Rehabilitation on the Stroke in Sequela Stage},

author = {Yangyang Miao and Shugeng Chen and Xinru Zhang and Jing Jin and Ren Xu and Ian Daly and Jie Jia and Xingyu Wang and Andrzej Cichockiand Tzyy-Ping Jung},

doi = {https://doi.org/10.1155/2020/8882764},

year  = {2020},

date = {2020-12-14},

journal = {Neural Plasticity},

keywords = {BCI, Motor imagery, stroke rehabilitation},

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{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{Daly2020data,

title = {Neural and physiological data from participants listening to affective music},

author = {Ian Daly and Nicoletta Nicolaou and Duncan Williams and Faustina Hwang and Alexis Kirke and Eduardo Miranda and Slawomir J. Nasuto},

year  = {2020},

date = {2020-05-07},

journal = {Scientific Data},

abstract = {Music provides a means of communicating affective meaning. However, the neurological mechanisms by which music induces affect are not fully understood. Our project sought to investigate this through a series of experiments into how humans react to affective musical stimuli and how physiological and neurological signals recorded from those participants change in accordance with self-reported changes in affect. In this paper, the datasets recorded over the course of this project are presented, including details of the musical stimuli, participant reports of their felt changes in affective states as they listened to the music, and concomitant recordings of physiological and neurological activity. We also include non-identifying meta data on our participant populations for purposes of further exploratory analysis. This data provides a large and valuable novel resource for researchers investigating emotion, music, and how they affect our neural and physiological activity.},

keywords = {Affective composition, Affective computing, BCI, BCMI, Data, EEG, Emotion, fMRI, Music},

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{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{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{Daly2019-fMRI,

title = {Electroencephalography reflects the activity of sub-cortical brain regions during approach-withdrawal behaviour while listening to music},

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

url = {https://www.nature.com/articles/s41598-019-45105-2},

doi = {https://doi.org/10.1038/s41598-019-45105-2},

year  = {2019},

date = {2019-06-03},

journal = {Scientific Reports},

keywords = {Affective composition, EEG, Emotion, fMRI, Music, Music generation},

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{Daly2019,

title = {Mathematical mindsets increase student motivation: Evidence from the EEG},

author = {Ian Daly and Jake Bourgaize and Alexei Vernitski},

year  = {2019},

date = {2019-04-11},

journal = {Trends in Neuroscience and Education},

keywords = {Affective computing, Education, EEG, Emotion, Mathematical mindsets},

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{WilliamsN2018,

title = {Markov Model-based method to analyse time-varying networks in EEG task-related data},

author = {Nitin Jonathan Williams and Ian Daly and Slawomir Nasuto},

year  = {2018},

date = {2018-08-20},

journal = {Frontiers in Computational Neuroscience},

keywords = {EEG, Functional connectivity},

pubstate = {published},

tppubtype = {article}

}