Publications
2023
Wu, Runze; Jin, Jing; Daly, Ian; Wang, Xingyu; Cichocki, Andrzej
Classification of motor imagery based on multi-scale feature extraction and the channel-temporal attention module Journal Article
In: IEEE Transactions on Neural Systems &Rehabilitation Engineering, 2023.
Links | BibTeX | Tags: BCI, Classification, EEG, Event related (de)/synchronisation, Motor imagery
@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}
}
2022
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}
}
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}
}
2021
Jin, Jing; Sun, Hao; Daly, Ian; Li, Shurui; Liu, Chang; Wang, Xingyu; Cichocki, Andrzej
A Novel Classification Framework Using the Graph Representations of Electroencephalogram for Motor Imagery based Brain-Computer Interface Journal Article
In: IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2021.
Abstract | Links | BibTeX | Tags: BCI, Classification, EEG, ERD, Functional connectivity, Motor imagery
@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}
}
Li, Shurui; Daly, Ian; Wang, Xingyu; Lam, Hak-Keung; Cichocki, Andrzej
Enhancing P300 based character recognition performance using a combination of ensemble classifiers and a fuzzy fusion method Journal Article
In: Journal of Neuroscience Methods, 2021.
BibTeX | Tags: BCI, Classification, EEG, ERP, Event-related potential, Fuzzy logic, P300
@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}
}
Daly, Ian
Removal of physiological artifacts from simultaneous EEG and fMRI recordings Journal Article
In: Clinical Neurophysiology, 2021.
BibTeX | Tags: Affective computing, Artefact removal, Classification, EEG, fMRI
@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}
}
Torres, Juan Ramirez; Daly, Ian
How to build a fast and accurate Code-Modulated Brain-Computer Interface Journal Article
In: Journal of Neural Engineering, 2021.
BibTeX | Tags: BCI, Classification, cVEP, EEG, ERP, Event-related potential, Feature selection
@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}
}
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; Liu, Chang; Daly, Ian; Miao, Yangyang; Li, Shurui; Wang, Xingyu; Cichocki, Andrzej
Bispectrum-based Channel Selection for Motor Imagery based Brain-Computer Interfacing Journal Article
In: IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2020.
Links | BibTeX | Tags: BCI, Classification, Feature selection, Motor imagery
@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}
}
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}
}
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.
Daly, Ian
Affective Brain-Computer Interfacing and Methods for Affective State Detection Book Chapter
In: Nam, Chang S.; Nijholt, Anton; Lotte, Fabien (Ed.): BRAIN-COMPUTER INTERFACES HANDBOOK Technological and Theoretical Advances , Chapter 8, 2017.
BibTeX | Tags: BCI, BCMI, Classification, EEG, Emotion, Music
@inbook{Daly2016chap,
title = {Affective Brain-Computer Interfacing and Methods for Affective State Detection},
author = {Ian Daly},
editor = {Chang S. Nam and Anton Nijholt and Fabien Lotte},
year = {2017},
date = {2017-07-25},
booktitle = {BRAIN-COMPUTER INTERFACES HANDBOOK Technological and Theoretical Advances
},
chapter = {8},
keywords = {BCI, BCMI, Classification, EEG, Emotion, Music},
pubstate = {published},
tppubtype = {inbook}
}
2016
Daly, Ian; Williams, Duncan; Kirke, Alexis; Weaver, James; Malik, Asad; Hwang, Faustina; Wairagkar, Maitreyee; Miranda, Eduardo; Nasuto, Slawomir J.
An Affective Brain-Computer Music Interface Conference
BCI meeting 2016, 2016.
BibTeX | Tags: Affective computing, BCMI, Classification, EEG, Music generation
@conference{Daly2016aBCMIconf,
title = {An Affective Brain-Computer Music Interface},
author = {Ian Daly and Duncan Williams and Alexis Kirke and James Weaver and Asad Malik and Faustina Hwang and Maitreyee Wairagkar and Eduardo Miranda and Slawomir J. Nasuto},
year = {2016},
date = {2016-06-01},
booktitle = {BCI meeting 2016},
keywords = {Affective computing, BCMI, Classification, EEG, Music generation},
pubstate = {published},
tppubtype = {conference}
}
Wairagkar, Maitreyee; Daly, Ian; Hayashi, Yoshikatsu; Nasuto, Slawomir
Autocorrelation based EEG Dynamics depicting Motor Intention Conference
BCI Meeting 2016, 2016.
BibTeX | Tags: Autocorrelation, BCI, Classification, EEG, ERD
@conference{Wairagkar2016,
title = {Autocorrelation based EEG Dynamics depicting Motor Intention },
author = {Maitreyee Wairagkar and Ian Daly and Yoshikatsu Hayashi and Slawomir Nasuto},
year = {2016},
date = {2016-06-01},
booktitle = {BCI Meeting 2016},
keywords = {Autocorrelation, BCI, Classification, EEG, ERD},
pubstate = {published},
tppubtype = {conference}
}
2015
Daly, Ian; Williams, Duncan; Malik, Asad; Weaver, James; Hwang, Faustina; Kirke, Alexis; Eduardo Miranda,; Nasuto, Slawomir J.
Identifying music-induced emotions from EEG for use in brain-computer music interfacing Conference
Proceedings of the 4th workshop on affective brain-computer interfaces at the ACII 2015, 2015.
Abstract | Links | BibTeX | Tags: Affective computing, BCMI, Classification, EEG, Music generation
@conference{Daly2015ACII,
title = {Identifying music-induced emotions from EEG for use in brain-computer music interfacing},
author = {Ian Daly and Duncan Williams and Asad Malik and James Weaver and Faustina Hwang and Alexis Kirke and Eduardo Miranda, and Slawomir J. Nasuto},
url = {https://www.computer.org/csdl/proceedings/acii/2015/9953/00/07344685.pdf},
year = {2015},
date = {2015-09-01},
booktitle = {Proceedings of the 4th workshop on affective brain-computer interfaces at the ACII 2015},
pages = {923-929},
abstract = {Brain-computer music interfaces (BCMI) provide a method to modulate an individuals affective state via the selection or generation of music according to their current affective state. Potential applications of such systems may include entertainment of therapeutic applications. We outline a proposed design for such a BCMI and seek a method for automatically differentiating different music induced affective states. Band-power features are explored for use in automatically identifying music-induced affective states. Additionally, a linear discriminant analysis classifier and a support vector machine are evaluated with respect to their ability to classify music induced affective states from the electroencephalogram recorded during a BCMI calibration task. Accuracies of up to 79.5% (p < 0.001) are achieved with the support vector machine.},
keywords = {Affective computing, BCMI, Classification, EEG, Music generation},
pubstate = {published},
tppubtype = {conference}
}
2014
Wairagkar, Maitreyee; Daly, Ian; Hayashi, Yoshikatsu; Nasuto, Slawomir
Novel single trial movement classification based on temporal dynamics of EEG Conference
Proceedings of the Graz Brain-computer interface conference 2014, 2014.
Abstract | Links | BibTeX | Tags: Autocorrelation, BCI, Classification, EEG, ERD, Motor imagery
@conference{Wairagkar2014,
title = {Novel single trial movement classification based on temporal dynamics of EEG},
author = {Maitreyee Wairagkar and Ian Daly and Yoshikatsu Hayashi and Slawomir Nasuto},
url = {http://centaur.reading.ac.uk/37412/1/Graz%20conference%202014-Final%20version.pdf},
year = {2014},
date = {2014-09-01},
booktitle = {Proceedings of the Graz Brain-computer interface conference 2014},
abstract = {Various complex oscillatory processes are involved in the generation of the motor command. The temporal dynamics of these processes were studied for movement detection from single trial electroencephalogram (EEG). Autocorrelation analysis was performed on the EEG signals to find robust markers of movement detection. The evolution of the autocorrelation function was characterised via the relaxation time of the autocorrelation by exponential curve fitting. It was observed that the decay constant of
the exponential curve increased during movement, indicating that the autocorrelation function decays slowly during motor execution. Significant differences were observed between movement and no moment tasks. Additionally, a linear discriminant analysis (LDA) classifier was used to identify movement trials with a peak accuracy of 74%. },
keywords = {Autocorrelation, BCI, Classification, EEG, ERD, Motor imagery},
pubstate = {published},
tppubtype = {conference}
}
the exponential curve increased during movement, indicating that the autocorrelation function decays slowly during motor execution. Significant differences were observed between movement and no moment tasks. Additionally, a linear discriminant analysis (LDA) classifier was used to identify movement trials with a peak accuracy of 74%.
2013
Jin, Jing; Sellers, Eric W.; Zhang, Yu; Daly, Ian; Wang, Xingyu; Cichocki, Andrzej
Whether generic model works for rapid ERP-based BCI calibration Journal Article
In: Journal of Neuroscience Methods, vol. 212, no. 1, pp. 94-99, 2013.
Abstract | Links | BibTeX | Tags: BCI, Classification, Event-related potentials, Generic model
@article{Jin2012,
title = {Whether generic model works for rapid ERP-based BCI calibration},
author = {Jing Jin and Eric W. Sellers and Yu Zhang and Ian Daly and Xingyu Wang and Andrzej Cichocki},
doi = {10.1016/j.jneumeth.2012.09.020},
year = {2013},
date = {2013-01-13},
journal = {Journal of Neuroscience Methods},
volume = {212},
number = {1},
pages = {94-99},
abstract = {Event-related potential (ERP)-based brain-computer interfacing (BCI) is an effective method of basic communication. However, collecting calibration data, and classifier training, detracts from the amount of time allocated for online communication. Decreasing calibration time can reduce preparation time thereby allowing for additional online use, potentially lower fatigue, and improved performance. Previous studies, using generic online training models which avoid offline calibration, afford more time for online spelling. Such studies have not examined the direct effects of the model on individual performance, and the training sequence exceeded the time reported here. The first goal of this work is to survey whether one generic model works for all subjects and the second goal is to show the performance of a generic model using an online training strategy when participants could use the generic model. The generic model was derived from 10 participant's data. An additional 11 participants were recruited for the current study. Seven of the participants were able to use the generic model during online training. Moreover, the generic model performed as well as models obtained from participant specific offline data with a mean training time of less than 2 min. However, four of the participants could not use this generic model, which shows that one generic mode is not generic for all subjects. More research on ERPs of subjects with different characteristics should be done, which would be helpful to build generic models for subject groups. This result shows a potential valuable direction for improving the BCI system.},
keywords = {BCI, Classification, Event-related potentials, Generic model},
pubstate = {published},
tppubtype = {article}
}
2012
Billinger, Martin; Daly, Ian; Kaiser, Vera; Jin, Jing; Allison, Brendan Z.; Müller-Putz, Gernot R.; Brunner, Clemens
Is it Significant? Guidelines for Reporting BCI Performance Book Chapter
In: Stephen Dunne Brendan Z. Allison, Robert Leeb (Ed.): pp. 333-354, Springer, 2012, ISBN: 978-3-642-29745-8.
Abstract | Links | BibTeX | Tags: BCI, Classification, Information transfer rate, Significance testing
@inbook{Billinger2012,
title = {Is it Significant? Guidelines for Reporting BCI Performance},
author = {Martin Billinger and Ian Daly and Vera Kaiser and Jing Jin and Brendan Z. Allison and Gernot R. Müller-Putz and Clemens Brunner},
editor = {Brendan Z. Allison, Stephen Dunne, Robert Leeb, José Del R. Millán, Anton Nijholt},
url = {http://link.springer.com/chapter/10.1007%2F978-3-642-29746-5_17},
doi = {10.1007/978-3-642-29746-5_17},
isbn = {978-3-642-29745-8},
year = {2012},
date = {2012-07-07},
pages = {333-354},
publisher = {Springer},
abstract = {Recent growth in brain-computer interface (BCI) research has increased pressure to report improved performance. However, different research groups report performance in different ways. Hence, it is essential that evaluation procedures are valid and reported in sufficient detail. In this chapter we give an overview of available performance measures such as classification accuracy, cohen’s kappa, information transfer rate (ITR), and written symbol rate. We show how to distinguish results from chance level using confidence intervals for accuracy or kappa. Furthermore, we point out common pitfalls when moving from offline to online analysis and provide a guide on how to conduct statistical tests on (BCI) results.},
keywords = {BCI, Classification, Information transfer rate, Significance testing},
pubstate = {published},
tppubtype = {inbook}
}
2008
Daly, Ian; Nasuto, Slawomir J.; Warwick, Kevin
Towards natural human computer interaction in BCI Conference
Proceedings of the International symposium on artificial intelligence and simulated behaviour, Aberdeen, UK, AISB 2008.
Abstract | Links | BibTeX | Tags: BCI, Classification, EEG, Speech
@conference{IanDalySlawomirJ.Nasuto2008,
title = {Towards natural human computer interaction in BCI},
author = {Ian Daly and Slawomir J. Nasuto and Kevin Warwick},
url = {http://www.iandaly.co.uk/wp-content/uploads/2016/01/Towards-natural-human-computer-interaction-in-BCI.pdf},
year = {2008},
date = {2008-09-01},
booktitle = {Proceedings of the International symposium on artificial intelligence and simulated behaviour, Aberdeen, UK},
organization = {AISB},
abstract = {BCI systems require correct classification of signals interpreted from the brain for useful operation. To this end this paper investigates a method proposed in [1] to correctly classify a series of images presented to a group of subjects in [2].
We show that it is possible to use the proposed methods to correctly recognise the original stimuli presented to a subject from analysis of their EEG. Additionally we use a verification set to show that the trained classification method can be applied to a different set of data.
We go on to investigate the issue of invariance in EEG signals. That is, the brain representation of similar stimuli is recognisable across different subjects.
Finally we consider the usefulness of the methods investigated towards an improved BCI system and discuss how it could potentially lead to great improvements in the ease of use for the end user by offering an alternative, more intuitive control based mode of operation.},
keywords = {BCI, Classification, EEG, Speech},
pubstate = {published},
tppubtype = {conference}
}
We show that it is possible to use the proposed methods to correctly recognise the original stimuli presented to a subject from analysis of their EEG. Additionally we use a verification set to show that the trained classification method can be applied to a different set of data.
We go on to investigate the issue of invariance in EEG signals. That is, the brain representation of similar stimuli is recognisable across different subjects.
Finally we consider the usefulness of the methods investigated towards an improved BCI system and discuss how it could potentially lead to great improvements in the ease of use for the end user by offering an alternative, more intuitive control based mode of operation.