@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{Liang2022,

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

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

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

year  = {2022},

date = {2022-10-10},

urldate = {2022-10-10},

journal = {Cognitive Neurodynamics},

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

pubstate = {published},

tppubtype = {article}

}

@article{Liu2022,

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

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

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

year  = {2022},

date = {2022-04-06},

urldate = {2022-04-06},

journal = {Journal of Neuroscience Methods},

volume = {375},

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

pubstate = {published},

tppubtype = {article}

}

@article{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{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{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{Jin2019NN,

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

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

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

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

year  = {2019},

date = {2019-07-15},

journal = {Neural Networks},

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

pubstate = {published},

tppubtype = {article}

}

@article{Feng2019,

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

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

year  = {2019},

date = {2019-04-18},

journal = {Computational Intelligence and Neuroscience},

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

pubstate = {published},

tppubtype = {article}

}

@article{Feng2018,

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

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

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

year  = {2018},

date = {2018-06-01},

journal = {Neural Networks},

volume = {102},

pages = {87-95},

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

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

pubstate = {published},

tppubtype = {article}

}

@article{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{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{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}

}

@article{Scherer2013,

title = {On the use of games for non-invasive EEG-based Functional Brain Mapping},

author = {Reinhold Scherer and Gunther Moitzi and Ian Daly and Gernot Müller-Putz},

url = {https://www.google.co.uk/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwivypDisKzKAhWDaxQKHSN7CVgQFgggMAA&url=http%3A%2F%2Fieeexplore.ieee.org%2Fiel7%2F4804728%2F6527957%2F06472046.pdf%3Farnumber%3D6472046&usg=AFQjCNF1qrlxBoeaT4OaHviOGyqy6IZmNg&sig2=lgQU_N9tXnpU7wRB3M9Efg},

doi = {10.1109/TCIAIG.2013.2250287},

year  = {2013},

date = {2013-06-01},

journal = {IEEE Transactions on Computational Intelligence and AI in Games},

volume = {5},

number = {2},

pages = {155-163},

abstract = {The use of statistical models and statistical inference

for characterizing the interplay between brain structures and

human behavior (functional brain mapping) is common in neuroscience.

Statistical methods, however, require the availability of

sufficiently large data sets. As a result, experimental paradigms

used to collect behavioral trials from individuals are data centered

and not user centered. This means that experimental paradigms

are tuned to collect as many trials as possible, are generally rather

demanding, and are not always motivating or engaging for individuals.

Subject cooperation and their compliance with the task may

decrease over time. Whenever possible, paradigms are designed

to control for factors such as fatigue, attention, and motivation.

In this paper, we propose the use of the Kinect motion tracking

sensor (Microsoft, Inc., Redmond, WA, USA) in a game-based

paradigm for noninvasive electroencephalogram (EEG)-based

functional motor mapping. Results from an experimental study

with able-bodied subjects playing a virtual ball game suggest

that the Kinect sensor is useful for isolating specific movements

during the interaction with the game, and that the computed EEG

patterns for hand and feet movements are in agreement with

results described in the literature},

keywords = {BCI, ERD, Games, Kinect, Motor imagery},

pubstate = {published},

tppubtype = {article}

}

@article{Kaiser2012stroke,

title = {Relationship between electrical brain responses to motor imagery and motor impairment in stroke.},

author = {Vera Kaiser and Ian Daly and Floriana Pichiorri and Donatella Mattia and Gernot R. Muller-Putz and Christa Neuper},

doi = {10.1161/​STROKEAHA.112.665489},

year  = {2012},

date = {2012-08-14},

journal = {Stroke},

volume = {43},

number = {10},

pages = {2735-2740},

abstract = {BACKGROUND AND PURPOSE:

New strategies like motor imagery based brain-computer interfaces, which use brain signals such as event-related desynchronization (ERD) or event-related synchronization (ERS) for motor rehabilitation after a stroke, are undergoing investigation. However, little is known about the relationship between ERD and ERS patterns and the degree of stroke impairment. The aim of this work was to clarify this relationship.

METHODS:

EEG during motor imagery and execution were measured in 29 patients with first-ever monolateral stroke causing any degree of motor deficit in the upper limb. The strength and laterality of the ERD or ERS patterns were correlated with the scores of the European Stroke Scale, the Medical Research Council, and the Modified Ashworth Scale.

RESULTS:

Mean age of the patients was 58 ± 15 years; mean time from the incident was 4 ± 4 months. Stroke lesions were cortical (n=8), subcortical (n=11), or mixed (n=10), attributable to either an ischemic event (n=26) or a hemorrhage (n=3), affecting the right (n=16) or left (n=13) hemisphere. Higher impairment was related to stronger ERD in the unaffected hemisphere and higher spasticity was related to stronger ERD in the affected hemisphere. Both were related to a relatively stronger ERS in the affected hemisphere.

CONCLUSIONS:

The results of this study may have implications for the design of potential poststroke rehabilitation interventions based on brain-computer interface technologies that use neurophysiological signals like ERD or ERS as neural substrates for the mutual interaction between brain and machine and, ultimately, help stroke patients to regain motor control.},

keywords = {BCI, ERD, Motor imagery, Stroke},

pubstate = {published},

tppubtype = {article}

}