Publications
2022
Liang, Wei; Jin, Jing; Daly, Ian; Sun, Hao; Wang, Xingyu; Cichocki, Andrzej
Novel channel selection model based on graph convolutional network for motor imagery Journal Article
In: Cognitive Neurodynamics, 2022.
Links | BibTeX | Tags: Channel selection, EEG, Event related (de)/synchronisation, Machine learning, Motor imagery
@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}
}
Wang, Zilu; Li, Jichun; Daly, Ian; Li, Junhua
Machine Learning for Multi-Action Classification of Lower Limbs for BCI Conference
5th International Conference on Computing, Electronics & Communications Engineering (iCCECE '22), 2022.
BibTeX | Tags: BCI, EEG, Machine learning, Motor imagery
@conference{Wang2022,
title = {Machine Learning for Multi-Action Classification of Lower Limbs for BCI},
author = {Zilu Wang and Jichun Li and Ian Daly and Junhua Li},
year = {2022},
date = {2022-08-05},
booktitle = {5th International Conference on Computing, Electronics & Communications Engineering
(iCCECE '22)},
keywords = {BCI, EEG, Machine learning, Motor imagery},
pubstate = {published},
tppubtype = {conference}
}
Liua, Chang; Jin, Jing; Daly, Ian; Sun, Hao; Huang, Yitao; Wang, Xingyu; AndrzejCichocki,
Bispectrum-based hybrid neural network for motor imagery classification Journal Article
In: Journal of Neuroscience Methods, vol. 375, 2022.
Links | BibTeX | Tags: Classification, Machine learning, Motor imagery
@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}
}
Milan Rybar, Ian Daly
Neural decoding of semantic concepts: A systematic literature review Journal Article
In: Journal of Neural Engineering, 2022.
BibTeX | Tags: BCI, Machine learning, Review, Semantic BCI, Semantic decoding, Systematic review
@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}
}
Liu, Chang; Jin, Jing; Daly, Ian; Li, Shurui; Sun, Hao; Huang, Yitao; Wang, Xingyu; Cichocki, Andrej
SincNet-Based Hybrid Neural Network for Motor Imagery EEG Decoding Journal Article
In: IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 30, 2022.
Links | BibTeX | Tags: Classification, Machine learning, Motor imagery
@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}
}
Fang, Hua; Jin, Jing; Daly, Ian; yu Wang, Xing
Feature Extraction Method Based on Filter Banks and Riemannian Tangent Space in Motor-Imagery BCI Journal Article
In: IEEE Journal of Biomedical and Health Informatics, 2022.
Links | BibTeX | Tags: BCI, EEG, Feature selection, Machine learning
@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}
}
2021
Jin, Jing; Fang, Hua; Daly, Ian; Xiao, Ruocheng; Miao, Yangyang; Wang, Xingyu; Cichocki, Andrzej
Optimization of Model Training Based on Iterative Minimum Covariance Determinant in Motor-Imagery BCI Journal Article
In: International Journal of Neural Systems, 2021.
BibTeX | Tags: BCI, Classification, EEG, ERD, Machine learning, Motor imagery
@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}
}
Miao, Yangyang; Jin, Jing; Daly, Ian; Zuo, Cili; Wang, Xingyu; Cichocki, Andrzej; Jung, Tzyy-Ping
Learning Common Time-Frequency-Spatial Patterns for Motor Imagery Classification Journal Article
In: IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2021.
Abstract | Links | BibTeX | Tags: BCI, Classification, EEG, ERD, Event-related potential, Machine learning, Motor imagery
@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}
}
2020
Daly, Ian
Neural component analysis: a spatial filter for electroencephalogram analysis Journal Article
In: Journal of Neuroscience Methods, 2020.
BibTeX | Tags: Classification, EEG, ERP, Event-related potential, Feature selection, Machine learning
@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}
}
Jin, Jing; Xiao, Ruocheng; Daly, Ian; Miao, Yangyang; Wang, Xingyu; Cichocki, Andrzej
Internal Feature Selection Method of CSP Based on L1-Norm and Dempster-Shafer Theory Journal Article
In: IEEE Transactions on Neural Networks and Learning Systems, 2020.
BibTeX | Tags: BCI, EEG, Event-related potential, Machine learning
@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}
}
Ian Daly Milan Rybar, Riccardo Poli
Potential pitfalls of widely used implementations of common spatial patterns Conference
EMBC2020, 2020.
BibTeX | Tags: Classification, EEG, Machine learning
@conference{Rybar2020,
title = {Potential pitfalls of widely used implementations of common spatial patterns},
author = {Milan Rybar, Ian Daly, Riccardo Poli},
year = {2020},
date = {2020-08-01},
booktitle = {EMBC2020},
keywords = {Classification, EEG, Machine learning},
pubstate = {published},
tppubtype = {conference}
}
2019
Jin, Jing; Li, Shurui; Daly, Ian; Miao, Yangyang; Liu, Chang; Wang, Xingyu; Cichocki, Andrzej
The Study of Generic Model Set for Reducing Calibration Time in P300-based Brain-Computer Interface Journal Article
In: IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2019.
Abstract | Links | BibTeX | Tags: BCI, ERP, Event-related potential, Machine learning, P300
@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}
}
Jin, Jing; Miao, Yangyang; Daly, Ian; Zuo, Cili; Hu, Dewen; Cichocki, Andrzej
Correlation-based channel selection and regularized feature optimization for MI-based BCI Journal Article
In: Neural Networks, 2019.
Links | BibTeX | Tags: BCI, Channel selection, EEG, Feature selection, Machine learning, Motor imagery
@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}
}
Feng, Jiankui; Jin, Jing; Daly, Ian; Zhou, Jiale; Niu, Yugang; Wang, Xingyu; Cichocki, Andrzej
An Optimized Channel Selection Method based on Multi-frequency CSP-rank for Motor Imagery-based BCI system Journal Article
In: Computational Intelligence and Neuroscience, 2019.
BibTeX | Tags: BCI, Feature selection, Machine learning, Motor imagery
@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}
}
2018
Feng, Jiankui; Yin, Erwei; Jin, Jing; Saab, Rami; Daly, Ian; Wang, Xingyu; Hu, Dewen; Cichocki, Andrzej
Towards correlation-based time window selection method for motor imagery BCIs Journal Article
In: Neural Networks, vol. 102, pp. 87-95, 2018.
Abstract | Links | BibTeX | Tags: BCI, Machine learning, Motor imagery
@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}
}
2017
Daly, Ian; Williams, Duncan; Malik, Asad; Weaver, James; Kirke, Alexis; Hwang, Faustina; Miranda, Eduardo; Nasuto, Slawomir J.
Personalised, Multi-modal, Affective State Detection for Hybrid Brain-Computer Music Interfacing Journal Article
In: IEEE Transactions on Affective Computing, 2017.
Abstract | BibTeX | Tags: Affective computing, BCI, Classification, Feature selection, Machine learning
@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}
}
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.
2014
Daly, Ian; Roesch, Etienne; Weaver, James; Nasuto, Slawomir J.
Machine learning to identify neural correlates of music and emotions Book Chapter
In: Eduardo Reck Miranda, Julien Castet (Ed.): pp. 89-103, Springer, 2014, ISBN: 978-1-4471-6583-5.
Abstract | Links | BibTeX | Tags: EEG, Emotion, Machine learning, Models of emotion, Music
@inbook{Daly2014mu,
title = {Machine learning to identify neural correlates of music and emotions},
author = {Ian Daly and Etienne Roesch and James Weaver and Slawomir J. Nasuto},
editor = {Eduardo Reck Miranda, Julien Castet},
url = {http://www.iandaly.co.uk/wp-content/uploads/2016/01/Machine-learning-to-identify-neural-correlates-of-music-and-emotions.pdf},
doi = {10.1007/978-1-4471-6584-2_5},
isbn = {978-1-4471-6583-5},
year = {2014},
date = {2014-10-04},
pages = {89-103},
publisher = {Springer},
abstract = {While music is widely understood to induce an emotional response in the listener, the exact nature of that response and its neural correlates are not yet fully explored. Furthermore, the large number of features which may be extracted from, and used to describe, neurological data, music stimuli, and emotional responses, means that the relationships between these datasets produced during music listening tasks or the operation of a brain–computer music interface (BCMI) are likely to be complex and multidimensional. As such, they may not be apparent from simple visual inspection of the data alone. Machine learning, which is a field of computer science that aims at extracting information from data, provides an attractive framework for uncovering stable relationships between datasets and has been suggested as a tool by which neural correlates of music and emotion may be revealed. In this chapter, we provide an introduction to the use of machine learning methods for identifying neural correlates of musical perception and emotion. We then provide examples of machine learning methods used to study the complex relationships between neurological activity, musical stimuli, and/or emotional responses.},
keywords = {EEG, Emotion, Machine learning, Models of emotion, Music},
pubstate = {published},
tppubtype = {inbook}
}
2013
Daly, Ian; Scherer, Reinhold; Müller-Putz, Gernot
A population search algorithm for multiple clustered solutions: application to EEG connectivity Conference
Proceedings of BiomMed 2013, 2013.
BibTeX | Tags: EEG, Feature selection, Machine learning, Population search
@conference{Daly2013,
title = {A population search algorithm for multiple clustered solutions: application to EEG connectivity},
author = {Ian Daly and Reinhold Scherer and Gernot Müller-Putz},
year = {2013},
date = {2013-11-01},
booktitle = {Proceedings of BiomMed 2013},
keywords = {EEG, Feature selection, Machine learning, Population search},
pubstate = {published},
tppubtype = {conference}
}
2011
Daly, Ian
Phase Synchronisation in Brain Computer Interfacing PhD Thesis
School of Systems Engineering, 2011.
Abstract | Links | BibTeX | Tags: Artefact removal, BCI, EEG, Feature selection, Functional connectivity, Machine learning, Neural mass models, Phase synchronisation, PhD, Significance testing, Thesis
@phdthesis{Daly2011a,
title = {Phase Synchronisation in Brain Computer Interfacing},
author = {Ian Daly},
url = {http://www.iandaly.co.uk/publications/thesis/Phase_Synchronisation_in_Brain_Computer_Interfacing.pdf},
year = {2011},
date = {2011-07-01},
pages = {1-262},
address = {University of Reading},
school = {School of Systems Engineering},
abstract = {Brain Computer Interfaces (BCIs) are an emerging area of research combining the Neuroscience, Computer Science, Engineering, Mathematics, Human Computer Interaction and Psychology research fields. A BCI enables an individual to exert control of a computer without activation of the efferent nervous system or the muscles. This allows individuals suffering with partial or complete paralysis and associated conditions which prevent muscle movement to control a computer and hence communicate and exert control over their environment.
This thesis first investigates tools for automatically removing artifacts from the Electroencephalogram (EEG), a signal commonly used in the control a BCI. Tools for measuring inter-regional connectivity patterns within the brain via phase synchronisation are then evaluated and extended to provide novel measures of inter-regional connectivity across the entire cortex.
Feature selection approaches are then introduced and evaluated before being applied to select good feature sets for the discrimination of connectivity patterns. These approaches are compared to Markov modelling approaches which model
and classify temporal dependencies in the data.
The resulting tool-set is applied to a novel BCI control paradigm based upon the detection of single finger taps. It is demonstrated that the connectivity features produce significantly better classification accuracies than can be achieved using conventional features traditionally applied in BCI.},
type = {PhD Thesis},
keywords = {Artefact removal, BCI, EEG, Feature selection, Functional connectivity, Machine learning, Neural mass models, Phase synchronisation, PhD, Significance testing, Thesis},
pubstate = {published},
tppubtype = {phdthesis}
}
This thesis first investigates tools for automatically removing artifacts from the Electroencephalogram (EEG), a signal commonly used in the control a BCI. Tools for measuring inter-regional connectivity patterns within the brain via phase synchronisation are then evaluated and extended to provide novel measures of inter-regional connectivity across the entire cortex.
Feature selection approaches are then introduced and evaluated before being applied to select good feature sets for the discrimination of connectivity patterns. These approaches are compared to Markov modelling approaches which model
and classify temporal dependencies in the data.
The resulting tool-set is applied to a novel BCI control paradigm based upon the detection of single finger taps. It is demonstrated that the connectivity features produce significantly better classification accuracies than can be achieved using conventional features traditionally applied in BCI.