Publications
2024
Rybar, Milan; Poli, Riccardo; Daly, Ian
Corrigendum: Decoding of semantic categories of imagined concepts of animals and tools in fNIRS Bachelor Thesis
2024.
BibTeX | Tags:
@bachelorthesis{Rybar2024,
title = {Corrigendum: Decoding of semantic categories of imagined concepts of animals and tools in fNIRS},
author = {Milan Rybar and Riccardo Poli and Ian Daly},
year = {2024},
date = {2024-04-22},
journal = {Journal of Neural Engineering},
keywords = {},
pubstate = {published},
tppubtype = {bachelorthesis}
}
Jin, Jing; Xu, Ruitian; Daly, Ian; Zhao, Xueqing; Wang, Xingyu; Cichocki, Andrzej
MOCNN: A Multi-scale Deep Convolutional Neural Network for ERP-based Brain-Computer Interfaces Journal Article
In: IEEE Transactions on Cybernetics, 2024.
BibTeX | Tags:
@article{Jin2024a,
title = {MOCNN: A Multi-scale Deep Convolutional Neural Network for ERP-based Brain-Computer Interfaces},
author = {Jing Jin and Ruitian Xu and Ian Daly and Xueqing Zhao and Xingyu Wang and Andrzej Cichocki},
year = {2024},
date = {2024-04-15},
journal = {IEEE Transactions on Cybernetics},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2023
Sun, Hao; Jin, Jing; Daly, Ian; Huang, Yitao; Zhao, Xueqing; Wang, Xingyu; Cichocki, Andrzej
Feature learning framework based on EEG graph self-attention networks for motor imagery BCI systems Journal Article
In: Journal of Neuroscience Methods, vol. 399, 2023.
@article{Sun2023,
title = {Feature learning framework based on EEG graph self-attention networks for motor imagery BCI systems},
author = {Hao Sun and Jing Jin and Ian Daly and Yitao Huang and Xueqing Zhao and Xingyu Wang and Andrzej Cichocki},
doi = {https://doi.org/10.1016/j.jneumeth.2023.109969},
year = {2023},
date = {2023-09-03},
journal = {Journal of Neuroscience Methods},
volume = {399},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Wang, Zilu; Daly, Ian; Li, Junhua
An Evaluation of Hybrid Deep Learning Models for Classifying Multiple Lower Limb Actions Conference
2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2023.
BibTeX | Tags:
@conference{Wang2023,
title = {An Evaluation of Hybrid Deep Learning Models for Classifying Multiple Lower Limb Actions},
author = {Zilu Wang and Ian Daly and Junhua Li},
year = {2023},
date = {2023-07-24},
booktitle = {2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
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}
}
Armani, Federica; Daly, Ian; Vernitski, Alexei; Gillmeister, Helge; Scherer, Reinhold
'Maths Anxiety and Cognitive States Monitoring for Neuroadaptive Learning Systems Using Electroencephalography Conference
2023 IEEE MetroXRAINE, 2023.
BibTeX | Tags:
@conference{Armani2023b,
title = {'Maths Anxiety and Cognitive States Monitoring for Neuroadaptive Learning Systems Using Electroencephalography},
author = {Federica Armani and Ian Daly and Alexei Vernitski and Helge Gillmeister and Reinhold Scherer},
year = {2023},
date = {2023-07-03},
booktitle = {2023 IEEE MetroXRAINE},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Armani, Federica; Daly, Ian; Vernitski, Alexei; Gillmeister, Helge; Scherer, Reinhold
Identification of Math Anxiety and Mental State Monitoring in Neuroadaptive Learning Systems Using Electroencephalography Conference
2023.
BibTeX | Tags: Affective computing, BCI, BCI EEG, EEG
@conference{Armani2023,
title = {Identification of Math Anxiety and Mental State Monitoring in Neuroadaptive Learning Systems Using Electroencephalography},
author = {Federica Armani and Ian Daly and Alexei Vernitski and Helge Gillmeister and Reinhold Scherer},
year = {2023},
date = {2023-04-17},
urldate = {2023-04-17},
journal = {2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering - IEEE MetroXRAINE 2023},
keywords = {Affective computing, BCI, BCI EEG, EEG},
pubstate = {published},
tppubtype = {conference}
}
Daly, Ian
Neural decoding of music from the EEG Journal Article
In: Scientific Reports, 2023.
@article{Daly2022-musicDecoding,
title = {Neural decoding of music from the EEG},
author = {Ian Daly},
doi = {10.1038/s41598-022-27361-x},
year = {2023},
date = {2023-01-12},
urldate = {2022-12-30},
journal = {Scientific Reports},
type = {Journal article},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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}
}
Al-Taie, Inas; Franco, Paola Di Giuseppantonio Di; Tymkiw, Michael; Williams, Duncan; Daly, Ian
Sonic Enhancement of Virtual Exhibits Journal Article
In: PLoS One, 2022.
BibTeX | Tags: Affective computing, Art, Music generation, Soundscaping
@article{Al-Taie2022,
title = {Sonic Enhancement of Virtual Exhibits},
author = {Inas Al-Taie and Paola Di Giuseppantonio Di Franco and Michael Tymkiw and Duncan Williams and Ian Daly},
year = {2022},
date = {2022-08-11},
journal = {PLoS One},
keywords = {Affective computing, Art, Music generation, Soundscaping},
pubstate = {published},
tppubtype = {article}
}
Armani, Federica; Daly, Ian; Gillmeister, Helge; Vernitski, Alexei; Scherer, Reinhold
BCIs for education: future steps for wider use Conference
The 3rd Neuroadaptive Technology Conference, NAT’22, 2022.
BibTeX | Tags: Affective computing, BCI, Education, Mathematics
@conference{Armani2022,
title = {BCIs for education: future steps for wider use},
author = {Federica Armani and Ian Daly and Helge Gillmeister and Alexei Vernitski and Reinhold Scherer},
year = {2022},
date = {2022-08-05},
booktitle = {The 3rd Neuroadaptive Technology Conference, NAT’22},
keywords = {Affective computing, BCI, Education, Mathematics},
pubstate = {published},
tppubtype = {conference}
}
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; 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; Jin, Jing; Daly, Ian; Liu, Chang; Cichocki, Andrzej
Feature Selection method based on Menger Curvature and LDA Theory for a P300 Brain-computer Interface Journal Article
In: Journal of Neural Engineering, 2021.
BibTeX | Tags: BCI, EEG, Feature selection
@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}
}
Williams, Duncan; Daly, Ian; Al-Taie, Inas; Franco, Paola Di Giuseppantonio Di; Tymkiw, Micheal
Neuro-curation: A case study on the use of sonic enhancement of virtual museum exhibits Conference
Audio Mostly 2021, 2021.
BibTeX | Tags: Affective computing, Emotion, Music generation
@conference{Williams2021,
title = {Neuro-curation: A case study on the use of sonic enhancement of virtual museum exhibits},
author = {Duncan Williams and Ian Daly and Inas Al-Taie and Paola Di Giuseppantonio Di Franco and Micheal Tymkiw},
year = {2021},
date = {2021-08-09},
publisher = {Audio Mostly 2021},
keywords = {Affective computing, Emotion, Music generation},
pubstate = {published},
tppubtype = {conference}
}
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}
}
Rybar, Milan; Poli, Riccardo; Daly, Ian
Decoding of semantic categories of imagined concepts of animals and tools in fNIRS Journal Article
In: Journal of Neural Engineering, 2021.
BibTeX | Tags: BCI, fNIRS, Semantic decoding
@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}
}
2020
Miao, Yangyang; Chen, Shugeng; Zhang, Xinru; Jin, Jing; Xu, Ren; Daly, Ian; Jia, Jie; Wang, Xingyu; Jung, Andrzej Cichockiand Tzyy-Ping
BCI-Based Rehabilitation on the Stroke in Sequela Stage Journal Article
In: Neural Plasticity, 2020.
Links | BibTeX | Tags: BCI, Motor imagery, stroke rehabilitation
@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}
}
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}
}
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}
}
Daly, Ian; Rybar, Milan
Neural component analysis for motor imagery classification Conference
EMBC2020, 2020.
BibTeX | Tags: EEG, ERD, Motor imagery
@conference{Daly2020,
title = {Neural component analysis for motor imagery classification},
author = {Ian Daly and Milan Rybar},
year = {2020},
date = {2020-08-01},
booktitle = {EMBC2020},
keywords = {EEG, ERD, Motor imagery},
pubstate = {published},
tppubtype = {conference}
}
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}
}
Daly, Ian; Nicolaou, Nicoletta; Williams, Duncan; Hwang, Faustina; Kirke, Alexis; Miranda, Eduardo; Nasuto, Slawomir J.
Neural and physiological data from participants listening to affective music Journal Article
In: Scientific Data, 2020.
Abstract | BibTeX | Tags: Affective composition, Affective computing, BCI, BCMI, Data, EEG, Emotion, fMRI, Music
@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}
}
Daly, Ian; Williams, Duncan
“Hello Computer, How Am I Feeling?”, Case Studies of Neural Technology to Measure Emotions Book Chapter
In: Springer, 2020, ISBN: 978-3-030-34783-3.
Links | BibTeX | Tags: Affective composition, Affective computing, BCI, Emotion, Music, Music generation
@inbook{Daly2020book,
title = {“Hello Computer, How Am I Feeling?”, Case Studies of Neural Technology to Measure Emotions},
author = {Ian Daly and Duncan Williams},
doi = {https://doi.org/10.1007/978-3-030-34784-0_11},
isbn = {978-3-030-34783-3},
year = {2020},
date = {2020-02-28},
publisher = {Springer},
keywords = {Affective composition, Affective computing, BCI, Emotion, Music, Music generation},
pubstate = {published},
tppubtype = {inbook}
}
Chen, Zongmei; Jin, Jing; Daly, Ian; Zuo, Cili; Wang, Xingyu; Cichocki, Andrzej
The Effects of Visual Attention on Tactile P300 BCI Journal Article
In: Computational Intelligence and Neuroscience, 2020.
BibTeX | Tags: BCI, P300, Tactile BCI
@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}
}
Li, Shurui; Jin, Jing; Daly, Ian; Zuo, Cili; Wang, Xingyu; Cichocki, Andrzej
Comparison of the ERP-Based BCI Performance Among Chromatic (RGB) Semitransparent Face Patterns Journal Article
In: Frontiers Neuroscience, vol. 14, no. 54, pp. 12, 2020.
Abstract | Links | BibTeX | Tags: BCI, ERP, Event-related potential
@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}
}
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.
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}
}
Daly, Ian; Williams, Duncan; Hwang, Faustina; Kirke, Alexis; Miranda, Eduardo; Nasuto, Slawomir J.
Electroencephalography reflects the activity of sub-cortical brain regions during approach-withdrawal behaviour while listening to music Journal Article
In: Scientific Reports, 2019.
Links | BibTeX | Tags: Affective composition, EEG, Emotion, fMRI, Music, Music generation
@article{Daly2019-fMRI,
title = {Electroencephalography reflects the activity of sub-cortical brain regions during approach-withdrawal behaviour while listening to music},
author = {Ian Daly and Duncan Williams and Faustina Hwang and Alexis Kirke and Eduardo Miranda and Slawomir J. Nasuto},
url = {https://www.nature.com/articles/s41598-019-45105-2},
doi = {https://doi.org/10.1038/s41598-019-45105-2},
year = {2019},
date = {2019-06-03},
journal = {Scientific Reports},
keywords = {Affective composition, EEG, Emotion, fMRI, Music, Music generation},
pubstate = {published},
tppubtype = {article}
}
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}
}
Daly, Ian; Bourgaize, Jake; Vernitski, Alexei
Mathematical mindsets increase student motivation: Evidence from the EEG Journal Article
In: Trends in Neuroscience and Education, 2019.
BibTeX | Tags: Affective computing, Education, EEG, Emotion, Mathematical mindsets
@article{Daly2019,
title = {Mathematical mindsets increase student motivation: Evidence from the EEG},
author = {Ian Daly and Jake Bourgaize and Alexei Vernitski},
year = {2019},
date = {2019-04-11},
journal = {Trends in Neuroscience and Education},
keywords = {Affective computing, Education, EEG, Emotion, Mathematical mindsets},
pubstate = {published},
tppubtype = {article}
}
Cheng, J; Jin, J; Daly, I; Zhang, Y; Wang, B; Wang, X; Cichocki, A
Effect of a combination of flip and zooming stimuli on the performance of a visual brain-computer interface for spelling Journal Article
In: Biomedical Engineering/Biomedizinische Technik, vol. 64, no. 1, pp. 29-38, 2019.
BibTeX | Tags: BCI, ERP, Event-related potential, Event-related potentials
@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}
}
2018
Williams, Nitin Jonathan; Daly, Ian; Nasuto, Slawomir
Markov Model-based method to analyse time-varying networks in EEG task-related data Journal Article
In: Frontiers in Computational Neuroscience, 2018.
BibTeX | Tags: EEG, Functional connectivity
@article{WilliamsN2018,
title = {Markov Model-based method to analyse time-varying networks in EEG task-related data},
author = {Nitin Jonathan Williams and Ian Daly and Slawomir Nasuto},
year = {2018},
date = {2018-08-20},
journal = {Frontiers in Computational Neuroscience},
keywords = {EEG, Functional connectivity},
pubstate = {published},
tppubtype = {article}
}
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}
}
Qiu, Zhaoyang; Chen, Shugeng; Daly, Ian; Wang, Jie Jia Xingyu; Jin, Jing
BCI-Based Strategies on Stroke Rehabilitation with Avatar and FES Feedback Journal Article
In: 2018.
BibTeX | Tags: BCI, stroke rehabilitation
@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}
}
Cheng, J; Jin, J; Daly, I; Zhang, Y; Wang, B; Wang, X; Cichocki, A
Effect of a combination of flip and zooming stimuli on the performance of a visual brain-computer interface for spelling. Journal Article
In: Biomed Tech (Berl), 2018.
Links | BibTeX | Tags: BCI, ERP, P300
@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}
}
Williams, Duncan; Daly, Ian
BCI for ensemble music making and performance: why and how (not how and why) Conference
Together in Music: Expression, Performance and Communication in Ensembles, National Centre for Early Music, York, 2018.
BibTeX | Tags: BCMI, EEG, Emotion, Music generation
@conference{Williams2018,
title = {BCI for ensemble music making and performance: why and how (not how and why)},
author = {Duncan Williams and Ian Daly},
year = {2018},
date = {2018-01-04},
booktitle = {Together in Music: Expression, Performance and Communication in Ensembles},
address = {National Centre for Early Music, York},
keywords = {BCMI, EEG, Emotion, Music generation},
pubstate = {published},
tppubtype = {conference}
}
2017
Daly, Ian; Blanchard, Caroline; Holmes, Nicholas
Cortical excitability correlates with the event-related desynchronization during brain-computer interface control Journal Article
In: Journal of Neural Engineering, 2017.
BibTeX | Tags: BCI, Cortical excitability, ERD, Motor imagery, TMS
@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}
}
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.
Nicolaou, Nicoletta; Malik, Asad; Daly, Ian; Weaver, James; Hwang, Faustina; Kirke, Alexis; Roesch, Etienne B; Williams, Duncan; Miranda, Eduardo Reck; Nasuto, Slawomir
Directed motor-auditory EEG connectivity is modulated by music tempo Journal Article
In: Frontiers in Human Neuroscience, 2017.
Abstract | Links | BibTeX | Tags:
@article{Nicolaou2017,
title = {Directed motor-auditory EEG connectivity is modulated by music tempo},
author = {Nicoletta Nicolaou and Asad Malik and Ian Daly and James Weaver and Faustina Hwang and Alexis Kirke and Etienne B Roesch and Duncan Williams and Eduardo Reck Miranda and Slawomir Nasuto},
url = {https://www.frontiersin.org/articles/10.3389/fnhum.2017.00502/abstract},
doi = {10.3389/fnhum.2017.00502},
year = {2017},
date = {2017-10-02},
journal = {Frontiers in Human Neuroscience},
abstract = {Beat perception is fundamental to how we experience music, and yet the mechanism behind this spontaneous building of the internal beat representation is largely unknown. Existing findings support links between the tempo (speed) of the beat and enhancement of electroencephalogram (EEG) activity at tempo-related frequencies, but there are no studies looking at how tempo may affect the underlying long-range interactions between EEG activity at different electrodes. The present study investigates these long-range interactions using EEG activity recorded from 21 volunteers listening to music stimuli played at 4 different tempi (50, 100, 150 and 200 beats per minute). The music stimuli consisted of piano excerpts designed to convey the emotion of ‘peacefulness’. Noise stimuli with an identical acoustic content to the music excerpts were also presented for comparison purposes. The brain activity interactions were characterized with the imaginary part of coherence (iCOH) in the frequency range 1.5-18 Hz (δ, θ, α, and lower β) between all pairs of EEG electrodes for the four tempi and the music/noise conditions, as well as a baseline resting state condition obtained at the start of the experimental task. Our findings can be summarized as follows: (a) there was an ongoing long-range interaction in the resting state engaging fronto-posterior areas; (b) this interaction was maintained in both music and noise, but its strength and directionality were modulated as a result of acoustic stimulation; (c) the topological patterns of iCOH were similar for music, noise and resting state, however statistically significant differences in strength and direction of iCOH were identified; and (d) tempo had an effect on the direction and strength of motor-auditory interactions. Our findings are in line with existing literature and illustrate a part of the mechanism by which musical stimuli with different tempi can entrain changes in cortical activity.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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}
}