@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{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{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{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{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{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{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{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{Qiu2018,

title = {BCI-Based Strategies on Stroke Rehabilitation with Avatar and FES Feedback },

author = {Zhaoyang Qiu and Shugeng Chen and Ian Daly and Jie Jia Xingyu Wang and Jing Jin},

year  = {2018},

date = {2018-06-01},

keywords = {BCI, stroke rehabilitation},

pubstate = {published},

tppubtype = {article}

}

@article{Cheng2018,

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},

doi = {10.1515/bmt-2017-0082},

year  = {2018},

date = {2018-02-13},

journal = {Biomed Tech (Berl)},

keywords = {BCI, ERP, P300},

pubstate = {published},

tppubtype = {article}

}

@article{Daly2017TMS,

title = {Cortical excitability correlates with the event-related desynchronization during brain-computer interface control},

author = {Ian Daly and Caroline Blanchard and Nicholas Holmes},

year  = {2017},

date = {2017-11-13},

journal = {Journal of Neural Engineering},

keywords = {BCI, Cortical excitability, ERD, Motor imagery, TMS},

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{JingDaly2017a,

title = {An improved P300 pattern in BCI to catch user’s attention},

author = {Jing Jin and Hanhan Zhang and Ian Daly and Xingyu Wang and Andrezej Chiciocki},

year  = {2017},

date = {2017-02-24},

journal = {Journal of Neural Engineering},

keywords = {BCI, P300},

pubstate = {published},

tppubtype = {article}

}

@article{Daly2016aBCMI,

title = {Affective Brain-Computer Music Interfacing},

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

year  = {2016},

date = {2016-06-21},

journal = {Journal of Neural Engineering},

volume = {(accepted)},

keywords = {aBCMI, Affective composition, BCI, BCMI, Case based reasoning, EEG, Emotion, Hybrid BCI, Music generation},

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{Huang2016,

title = {An exploration of spatial auditory BCI paradigms with different sounds: Music vs Beeps},

author = {Minqiang Huang and Ian Daly and Jing Jin and Yu Zhang and Xingyu Wang and Andrzej Cichocki},

year  = {2016},

date = {2016-01-20},

journal = {Cognitive Neurodynamics},

keywords = {BCI, Event-related potentials},

pubstate = {published},

tppubtype = {article}

}

@article{Chen2016,

title = {Exploring combinations of different color and facial expression stimuli for gaze-independent BCIs},

author = {Long Chen and Jing Jin and Ian Daly and Yu Zhang and Xingyu Wang and Andrzej Cichocki},

url = {http://journal.frontiersin.org/article/10.3389/fncom.2016.00005/abstract},

doi = {10.3389/fncom.2016.00005},

year  = {2016},

date = {2016-01-11},

journal = {Frontiers in Computational Neuroscience},

keywords = {BCI, Face expression change, Gaze independent BCI},

pubstate = {published},

tppubtype = {article}

}

@article{Daly2014a,

title = {FORCe: Fully Online and automated artifact Removal for brain-Computer interfacing},

author = {Ian Daly and Martin Billinger and Reinhold Scherer and Gernot Müller-Putz},

url = {http://www.iandaly.co.uk/wp-content/uploads/2016/01/FORCe-fully-online-and-automated-artifact-removal-for-brain-computer-interfacing.pdf},

doi = {10.1109/TNSRE.2014.2346621},

year  = {2015},

date = {2015-09-01},

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

volume = {23},

number = {5},

pages = {725-736},

abstract = {A fully automated and online artifact removal method for the electroencephalogram (EEG) is developed for use in brain-computer interfacing (BCI). The method (FORCe) is based upon a novel combination of wavelet decomposition, independent component analysis, and thresholding. FORCe is able to operate on a small channel set during online EEG acquisition and does not require additional signals (e.g., electrooculogram signals). Evaluation of FORCe is performed offline on EEG recorded from 13 BCI particpants with cerebral palsy (CP) and online with three healthy participants. The method outperforms the state-of the-art automated artifact removal methods Lagged Auto-Mutual Information Clustering (LAMIC) and Fully Automated Statistical Thresholding for EEG artifact Rejection (FASTER), and is able to remove a wide range of artifact types including blink, electromyogram (EMG), and electrooculogram (EOG) artifacts.},

keywords = {Artefact removal, BCI, EEG, ICA, Tools, Wavelets},

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}

}

@article{Daly2014tempoBCI,

title = {Investigating music tempo as a feedback mechanism for closed-loop BCI control},

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

url = {http://www.iandaly.co.uk/wp-content/uploads/2016/01/tempoBCI.pdf},

doi = {10.1080/2326263X.2014.979728},

year  = {2014},

date = {2014-10-17},

journal = {Brain-Computer Interfaces},

volume = {1},

number = {3},

pages = {158-169},

abstract = {The feedback mechanism used in a brain-computer interface (BCI) forms an integral part of the closed-loop learning process required for successful operation of a BCI. However, ultimate success of the BCI may be dependent upon the modality of the feedback used. This study explores the use of music tempo as a feedback mechanism in BCI and compares it to the more commonly used visual feedback mechanism. Three different feedback modalities are compared for a kinaesthetic motor imagery BCI: visual, auditory via music tempo, and a combined visual and auditory feedback modality. Visual feedback is provided via the position, on the y-axis, of a moving ball. In the music feedback condition, the tempo of a piece of continuously generated music is dynamically adjusted via a novel music-generation method. All the feedback mechanisms allowed users to learn to control the BCI. However, users were not able to maintain as stable control with the music tempo feedback condition as they could in the visual feedback and combined conditions. Additionally, the combined condition exhibited significantly less inter-user variability, suggesting that multi-modal feedback may lead to more robust results. Finally, common spatial patterns are used to identify participant-specific spatial filters for each of the feedback modalities. The mean optimal spatial filter obtained for the music feedback condition is observed to be more diffuse and weaker than the mean spatial filters obtained for the visual and combined feedback conditions.},

keywords = {BCI, BCMI, ERD, Motor imagery, Music, Tempo},

pubstate = {published},

tppubtype = {article}

}

@article{Daly2014,

title = {Exploration of the neural correlates of cerebral palsy for sensorimotor BCI control},

author = {Ian Daly and Josef Faller and Reinhold Scherer and Catherine Sweeney-Reed and Slawomir J. Nasuto and Martin Billinger and Gernot Müller-Putz},

url = {http://www.iandaly.co.uk/wp-content/uploads/2016/01/Exploration-of-the-neural-correlates-of-cerebral-palsy-for-sensorimotor-BCI-control.pdf},

doi = {10.3389/fneng.2014.00020},

year  = {2014},

date = {2014-07-09},

journal = {Frontiers in Neuroengineering},

volume = {7},

number = {20},

abstract = {Cerebral palsy (CP) includes a broad range of disorders, which can result in impairment of posture and movement control. Brain-computer interfaces (BCIs) have been proposed as assistive devices for individuals with CP. Better understanding of the neural processing underlying motor control in affected individuals could lead to more targeted BCI rehabilitation and treatment options. We have explored well-known neural correlates of movement, including event-related desynchronization (ERD), phase synchrony, and a recently-introduced measure of phase dynamics, in participants with CP and healthy control participants. Although present, significantly less ERD and phase locking were found in the group with CP. Additionally, inter-group differences in phase dynamics were also significant. Taken together these findings suggest that users with CP exhibit lower levels of motor cortex activation during motor imagery, as reflected in lower levels of ongoing mu suppression and less functional connectivity. These differences indicate that development of BCIs for individuals with CP may pose additional challenges beyond those faced in providing BCIs to healthy individuals.},

keywords = {BCI, Cerebral palsy, ERD, Motor imagery},

pubstate = {published},

tppubtype = {article}

}

@article{Muller-Putz2014,

title = {Motor imagery induced EEG patterns in spinal cord injury patients and their impact on Brain-Computer Interface accuracy},

author = {Gernot Müller-Putz and Ian Daly and Vera Kaiser},

url = {http://www.ncbi.nlm.nih.gov/pubmed/24835837},

doi = {10.1088/1741-2560/11/3/035011},

year  = {2014},

date = {2014-06-01},

journal = {Journal of Neural Engineering},

volume = {11},

number = {3},

pages = {1-9},

abstract = {OBJECTIVE:

Assimilating the diagnosis complete spinal cord injury (SCI) takes time and is not easy, as patients know that there is no 'cure' at the present time. Brain-computer interfaces (BCIs) can facilitate daily living. However, inter-subject variability demands measurements with potential user groups and an understanding of how they differ to healthy users BCIs are more commonly tested with. Thus, a three-class motor imagery (MI) screening (left hand, right hand, feet) was performed with a group of 10 able-bodied and 16 complete spinal-cord-injured people (paraplegics, tetraplegics) with the objective of determining what differences were present between the user groups and how they would impact upon the ability of these user groups to interact with a BCI.

APPROACH:

Electrophysiological differences between patient groups and healthy users are measured in terms of sensorimotor rhythm deflections from baseline during MI, electroencephalogram microstate scalp maps and strengths of inter-channel phase synchronization. Additionally, using a common spatial pattern algorithm and a linear discriminant analysis classifier, the classification accuracy was calculated and compared between groups.

MAIN RESULTS:

It is seen that both patient groups (tetraplegic and paraplegic) have some significant differences in event-related desynchronization strengths, exhibit significant increases in synchronization and reach significantly lower accuracies (mean (M) = 66.1%) than the group of healthy subjects (M = 85.1%).

SIGNIFICANCE:

The results demonstrate significant differences in electrophysiological correlates of motor control between healthy individuals and those individuals who stand to benefit most from BCI technology (individuals with SCI). They highlight the difficulty in directly translating results from healthy subjects to participants with SCI and the challenges that, therefore, arise in providing BCIs to such individuals.},

keywords = {BCI, ERD, Functional connectivity, Motor imagery, SCI},

pubstate = {published},

tppubtype = {article}

}

@article{Jin2014,

title = {An optimized ERP Brain-computer interface based on facial expression changes},

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

url = {http://iopscience.iop.org/article/10.1088/1741-2560/11/3/036004/pdf},

doi = {10.1088/1741-2560/11/3/036004},

year  = {2014},

date = {2014-06-01},

journal = {Journal of Neural Engineering},

volume = {11},

number = {3},

pages = {1-11},

abstract = {OBJECTIVE:

Interferences from spatially adjacent non-target stimuli are known to evoke event-related potentials (ERPs) during non-target flashes and, therefore, lead to false positives. This phenomenon was commonly seen in visual attention-based brain-computer interfaces (BCIs) using conspicuous stimuli and is known to adversely affect the performance of BCI systems. Although users try to focus on the target stimulus, they cannot help but be affected by conspicuous changes of the stimuli (such as flashes or presenting images) which were adjacent to the target stimulus. Furthermore, subjects have reported that conspicuous stimuli made them tired and annoyed. In view of this, the aim of this study was to reduce adjacent interference, annoyance and fatigue using a new stimulus presentation pattern based upon facial expression changes. Our goal was not to design a new pattern which could evoke larger ERPs than the face pattern, but to design a new pattern which could reduce adjacent interference, annoyance and fatigue, and evoke ERPs as good as those observed during the face pattern.

APPROACH:

Positive facial expressions could be changed to negative facial expressions by minor changes to the original facial image. Although the changes are minor, the contrast is big enough to evoke strong ERPs. In this paper, a facial expression change pattern between positive and negative facial expressions was used to attempt to minimize interference effects. This was compared against two different conditions, a shuffled pattern containing the same shapes and colours as the facial expression change pattern, but without the semantic content associated with a change in expression, and a face versus no face pattern. Comparisons were made in terms of classification accuracy and information transfer rate as well as user supplied subjective measures.

MAIN RESULTS:

The results showed that interferences from adjacent stimuli, annoyance and the fatigue experienced by the subjects could be reduced significantly (p < 0.05) by using the facial expression change patterns in comparison with the face pattern. The offline results show that the classification accuracy of the facial expression change pattern was significantly better than that of the shuffled pattern (p < 0.05) and the face pattern (p < 0.05).

SIGNIFICANCE:

The facial expression change pattern presented in this paper reduced interference from adjacent stimuli and decreased the fatigue and annoyance experienced by BCI users significantly (p < 0.05) compared to the face pattern.},

keywords = {BCI, Event-related potentials, Facial expressions},

pubstate = {published},

tppubtype = {article}

}

@article{Daly2013cpBCI,

title = {On the control of Brain-computer interfaces by users with Cerebral palsy},

author = {Ian Daly and Martin Billinger and Jose Laparra-Hernandez and Fabio Aloise and Mariano Lloria Garcia and Josef Faller and Reinhold Scherer and Gernot Muller-Putz},

url = {http://www.iandaly.co.uk/wp-content/uploads/2016/01/draft_6-0.pdf},

doi = {10.1016/j.clinph.2013.02.118},

year  = {2013},

date = {2013-09-01},

journal = {Clinical Neurophysiology},

volume = {124},

number = {9},

pages = {1787-1797},

abstract = {OBJECTIVE:

Brain-computer interfaces (BCIs) have been proposed as a potential assistive device for individuals with cerebral palsy (CP) to assist with their communication needs. However, it is unclear how well-suited BCIs are to individuals with CP. Therefore, this study aims to investigate to what extent these users are able to gain control of BCIs.

METHODS:

This study is conducted with 14 individuals with CP attempting to control two standard online BCIs (1) based upon sensorimotor rhythm modulations, and (2) based upon steady state visual evoked potentials.

RESULTS:

Of the 14 users, 8 are able to use one or other of the BCIs, online, with a statistically significant level of accuracy, without prior training. Classification results are driven by neurophysiological activity and not seen to correlate with occurrences of artifacts. However, many of these users' accuracies, while statistically significant, would require either more training or more advanced methods before practical BCI control would be possible.

CONCLUSIONS:

The results indicate that BCIs may be controlled by individuals with CP but that many issues need to be overcome before practical application use may be achieved.

SIGNIFICANCE:

This is the first study to assess the ability of a large group of different individuals with CP to gain control of an online BCI system. The results indicate that six users could control a sensorimotor rhythm BCI and three a steady state visual evoked potential BCI at statistically significant levels of accuracy (SMR accuracies; mean ± STD, 0.821 ± 0.116, SSVEP accuracies; 0.422 ± 0.069).},

keywords = {BCI, Cerebral palsy, ERD, Motor imagery, SSVEP},

pubstate = {published},

tppubtype = {article}

}