@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{Fang2022,

title = {Feature Extraction Method Based on Filter Banks and Riemannian Tangent Space in Motor-Imagery BCI},

author = {Hua Fang and Jing Jin and Ian Daly and Xing yu Wang},

doi = {10.1109/JBHI.2022.3146274},

year  = {2022},

date = {2022-01-31},

journal = {IEEE Journal of Biomedical and Health Informatics},

keywords = {BCI, EEG, Feature selection, Machine learning},

pubstate = {published},

tppubtype = {article}

}

@article{Jin2021Graph,

title = {A Novel Classification Framework Using the Graph Representations of Electroencephalogram for Motor Imagery based Brain-Computer Interface},

author = {Jing Jin and Hao Sun and Ian Daly and Shurui Li and Chang Liu and Xingyu Wang and Andrzej Cichocki},

doi = {10.1109/TNSRE.2021.3139095},

year  = {2021},

date = {2021-12-28},

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

abstract = {The motor imagery (MI) based brain-computer interfaces (BCIs) have been proposed as a potential physical rehabilitation technology. However, the low classification accuracy achievable with MI tasks is still a challenge when building effective BCI systems. We propose a novel MI classification model based on measurement of functional connectivity between brain regions and graph theory. Specifically, motifs describing local network structures in the brain are extracted from functional connectivity graphs. A graph embedding model called Ego-CNNs is then used to build a classifier, which can convert the graph from a structural representation to a fixed-dimensional vector for detecting critical structure in the graph. We validate our proposed method on four datasets, and the results show that our proposed method produces high classification accuracies in two-class classification tasks (92.8% for dataset 1, 93.4% for dataset 2, 96.5% for dataset 3, and 80.2% for dataset 4) and multiclass classification tasks (90.33% for dataset 1). Our proposed method achieves a mean Kappa value of 0.88 across nine participants, which is superior to other methods we compared it to. These results indicate that there is a local structural difference in functional connectivity graphs extracted under different motor imagery tasks. Our proposed method has great potential for motor imagery classification in future studies.},

keywords = {BCI, Classification, EEG, ERD, Functional connectivity, Motor imagery},

pubstate = {published},

tppubtype = {article}

}

@article{Li2021Menger,

title = {Feature Selection method based on Menger Curvature and LDA Theory for a P300 Brain-computer Interface},

author = {Shurui Li and Jing Jin and Ian Daly and Chang Liu and Andrzej Cichocki},

year  = {2021},

date = {2021-11-29},

journal = {Journal of Neural Engineering},

keywords = {BCI, EEG, Feature selection},

pubstate = {published},

tppubtype = {article}

}

@article{Li2021,

title = {Enhancing P300 based character recognition performance using a combination of ensemble classifiers and a fuzzy fusion method},

author = {Shurui Li and Ian Daly and Xingyu Wang and Hak-Keung Lam and Andrzej Cichocki},

year  = {2021},

date = {2021-08-05},

journal = {Journal of Neuroscience Methods},

keywords = {BCI, Classification, EEG, ERP, Event-related potential, Fuzzy logic, P300},

pubstate = {published},

tppubtype = {article}

}

@article{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}

}

@article{Ramirez-Torres2021,

title = {How to build a fast and accurate Code-Modulated Brain-Computer Interface},

author = {Juan Ramirez Torres and Ian Daly},

year  = {2021},

date = {2021-04-21},

journal = {Journal of Neural Engineering},

keywords = {BCI, Classification, cVEP, EEG, ERP, Event-related potential, Feature selection},

pubstate = {published},

tppubtype = {article}

}

@article{Jin2021optMod,

title = {Optimization of Model Training Based on Iterative Minimum Covariance Determinant in Motor-Imagery BCI},

author = {Jing Jin and Hua Fang and Ian Daly and Ruocheng Xiao and Yangyang Miao and Xingyu Wang and Andrzej Cichocki},

year  = {2021},

date = {2021-04-18},

journal = {International Journal of Neural Systems},

keywords = {BCI, Classification, EEG, ERD, Machine learning, Motor imagery},

pubstate = {published},

tppubtype = {article}

}

@article{Miao2021,

title = {Learning Common Time-Frequency-Spatial Patterns for Motor Imagery Classification},

author = {Yangyang Miao and Jing Jin and Ian Daly and Cili Zuo and Xingyu Wang and Andrzej Cichocki and Tzyy-Ping Jung},

doi = {10.1109/TNSRE.2021.3071140},

year  = {2021},

date = {2021-04-05},

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

abstract = {The common spatial patterns (CSP) algorithm is the most popular spatial filtering method applied to extract electroencephalogram (EEG) features for motor imagery (MI) based brain-computer interface (BCI) systems. The effectiveness of the CSP algorithm depends on optimal selection of the frequency band and time window from the EEG. Many algorithms have been designed to optimize frequency band selection for CSP, while few algorithms seek to optimize the time window. This study proposes a novel framework, termed common time-frequency-spatial patterns (CTFSP), to extract sparse CSP features from multi-band filtered EEG data in multiple time windows. Specifically, the whole MI period is first segmented into multiple subseries using a sliding time window approach. Then, sparse CSP features are extracted from multiple frequency bands in each time window. Finally, multiple support vector machine (SVM) classifiers with the Radial Basis Function (RBF) kernel are trained to identify the MI tasks and the voting result of these classifiers determines the final output of the BCI. This study applies the proposed CTFSP algorithm to three public EEG datasets (BCI competition III dataset IVa, BCI competition III dataset IIIa, and BCI competition IV dataset 1) to validate its effectiveness, compared against several other state-of-the-art methods. The experimental results demonstrate that the proposed algorithm is a promising candidate for improving the performance of MI-BCI systems.},

keywords = {BCI, Classification, EEG, ERD, Event-related potential, Machine learning, Motor imagery},

pubstate = {published},

tppubtype = {article}

}

@article{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}

}

@article{Jing2020CSPDempster-Shafer,

title = {Internal Feature Selection Method of CSP Based on L1-Norm and Dempster-Shafer Theory},

author = {Jing Jin and Ruocheng Xiao and Ian Daly and Yangyang Miao and Xingyu Wang and Andrzej Cichocki},

year  = {2020},

date = {2020-08-05},

journal = {IEEE Transactions on Neural Networks and Learning Systems},

keywords = {BCI, EEG, Event-related potential, Machine learning},

pubstate = {published},

tppubtype = {article}

}

@article{Daly2020data,

title = {Neural and physiological data from participants listening to affective music},

author = {Ian Daly and Nicoletta Nicolaou and Duncan Williams and Faustina Hwang and Alexis Kirke and Eduardo Miranda and Slawomir J. Nasuto},

year  = {2020},

date = {2020-05-07},

journal = {Scientific Data},

abstract = {Music provides a means of communicating affective meaning. However, the neurological mechanisms by which music induces affect are not fully understood. Our project sought to investigate this through a series of experiments into how humans react to affective musical stimuli and how physiological and neurological signals recorded from those participants change in accordance with self-reported changes in affect. In this paper, the datasets recorded over the course of this project are presented, including details of the musical stimuli, participant reports of their felt changes in affective states as they listened to the music, and concomitant recordings of physiological and neurological activity. We also include non-identifying meta data on our participant populations for purposes of further exploratory analysis. This data provides a large and valuable novel resource for researchers investigating emotion, music, and how they affect our neural and physiological activity.},

keywords = {Affective composition, Affective computing, BCI, BCMI, Data, EEG, Emotion, fMRI, Music},

pubstate = {published},

tppubtype = {article}

}

@article{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{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}

}

@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}

}

@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}

}

@article{Daly2016aBCMI,

title = {Affective Brain-Computer Music Interfacing},

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

year  = {2016},

date = {2016-06-21},

journal = {Journal of Neural Engineering},

volume = {(accepted)},

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

pubstate = {published},

tppubtype = {article}

}

@article{DalyPred2015,

title = {Music-induced emotions can be predicted from a combination of brain activity and acoustic features},

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

url = {http://www.iandaly.co.uk/wp-content/uploads/2016/01/music-induced-emotions-can-be-predicted-from-a-combination-of-brain-activity-and-acoustic-features.pdf},

doi = {10.1016/j.bandc.2015.08.003},

year  = {2015},

date = {2015-12-01},

journal = {Brain and Cognition},

volume = {101},

pages = {1-11},

abstract = {It is widely acknowledged that music can communicate and induce a wide range of emotions in the listener. However, music is a highly-complex audio signal composed of a wide range of complex time- and frequency-varying components. Additionally, music-induced emotions are known to differ greatly between listeners. Therefore, it is not immediately clear what emotions will be induced in a given individual by a piece of music. We attempt to predict the music-induced emotional response in a listener by measuring the activity in the listeners electroencephalogram (EEG). We combine these measures with acoustic descriptors of the music, an approach that allows us to consider music as a complex set of time-varying acoustic features, independently of any specific music theory. Regression models are found which allow us to predict the music-induced emotions of our participants with a correlation between the actual and predicted responses of up to r=0.234,p<0.001. This regression fit suggests that over 20% of the variance of the participant's music induced emotions can be predicted by their neural activity and the properties of the music. Given the large amount of noise, non-stationarity, and non-linearity in both EEG and music, this is an encouraging result. Additionally, the combination of measures of brain activity and acoustic features describing the music played to our participants allows us to predict music-induced emotions with significantly higher accuracies than either feature type alone (p<0.01).},

keywords = {Acoustic features, EEG, Emotion, Music},

pubstate = {published},

tppubtype = {article}

}

@article{Daly2014a,

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

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

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

doi = {10.1109/TNSRE.2014.2346621},

year  = {2015},

date = {2015-09-01},

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

volume = {23},

number = {5},

pages = {725-736},

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

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

pubstate = {published},

tppubtype = {article}

}

@article{Daly2014_featSel,

title = {Automated identification of neural correlates of continuous variables},

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

url = {http://www.iandaly.co.uk/wp-content/uploads/2016/01/Automated-identification-of-neural-correlates-of-continous-variables.pdf},

doi = {10.1016/j.jneumeth.2014.12.012},

year  = {2015},

date = {2015-03-15},

journal = {Journal of Neuroscience Methods},

volume = {242},

pages = { 65–71},

abstract = {Background



The electroencephalogram (EEG) may be described by a large number of different feature types and automated feature selection methods are needed in order to reliably identify features which correlate with continuous independent variables.



New method



A method is presented for the automated identification of features that differentiate two or more groups in neurological datasets based upon a spectral decomposition of the feature set. Furthermore, the method is able to identify features that relate to continuous independent variables.



Results



The proposed method is first evaluated on synthetic EEG datasets and observed to reliably identify the correct features. The method is then applied to EEG recorded during a music listening task and is observed to automatically identify neural correlates of music tempo changes similar to neural correlates identified in a previous study. Finally, the method is applied to identify neural correlates of music-induced affective states. The identified neural correlates reside primarily over the frontal cortex and are consistent with widely reported neural correlates of emotions.



Comparison with existing methods



The proposed method is compared to the state-of-the-art methods of canonical correlation analysis and common spatial patterns, in order to identify features differentiating synthetic event-related potentials of different amplitudes and is observed to exhibit greater performance as the number of unique groups in the dataset increases.



Conclusions



The proposed method is able to identify neural correlates of continuous variables in EEG datasets and is shown to outperform canonical correlation analysis and common spatial patterns.},

keywords = {EEG, Feature selection},

pubstate = {published},

tppubtype = {article}

}

@article{Daly2014NC,

title = {Neural correlates of emotional responses to music: an EEG study},

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

url = {http://www.iandaly.co.uk/wp-content/uploads/2016/01/Neural-correlates-of-emotional-responses-to-music-an-EEG-study-pub.pdf},

doi = {10.1016/j.neulet.2014.05.003},

year  = {2014},

date = {2014-06-24},

journal = {Neuroscience letters},

volume = {573},

pages = {52–57},

abstract = {This paper presents an EEG study into the neural correlates of music-induced emotions. We presented participants with a large dataset containing musical pieces in different styles, and asked them to report on their induced emotional responses.



We found neural correlates of music-induced emotion in a number of frequencies over the pre-frontal cortex. Additionally, we found a set of patterns of functional connectivity, defined by inter-channel coherence measures, to be significantly different between groups of music-induced emotional responses.},

keywords = {Asymmetry, EEG, Emotion, Functional connectivity, Music},

pubstate = {published},

tppubtype = {article}

}

@article{Daly2013artComp,

title = {Automated artifact removal from the electroencephalogram; a comparative study},

author = {Ian Daly and Nicoletta Nicolaou and Slawomir J. Nasuto and Kevin Warwick},

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

doi = {10.1177/1550059413476485},

year  = {2013},

date = {2013-10-01},

journal = {Clinical EEG and Neuroscience},

volume = {44},

number = {4},

pages = {291-306},

abstract = {Contamination of the electroencephalogram (EEG) by artifacts greatly reduces the quality of the recorded signals. There is a need for automated artifact removal methods. However, such methods are rarely evaluated against one another via rigorous criteria, with results often presented based upon visual inspection alone. This work presents a comparative study of automatic methods for removing blink, electrocardiographic, and electromyographic artifacts from the EEG. Three methods are considered; wavelet, blind source separation (BSS), and multivariate singular spectrum analysis (MSSA)-based correction. These are applied to data sets containing mixtures of artifacts. Metrics are devised to measure the performance of each method. The BSS method is seen to be the best approach for artifacts of high signal to noise ratio (SNR). By contrast, MSSA performs well at low SNRs but at the expense of a large number of false positive corrections.},

keywords = {Artefact removal, EEG, ICA, MSSA, Wavelets},

pubstate = {published},

tppubtype = {article}

}

@article{Daly2012phase,

title = {Testing for significance of phase synchronisation dynamics in the EEG},

author = {Ian Daly and Catherine Sweeney-Reed and Slawomir J. Nasuto},

url = {http://www.iandaly.co.uk/wp-content/uploads/2016/01/Testing-for-significance-of-phase-synchronisation-dynamics-in.pdf},

doi = {10.1007/s10827-012-0428-2},

year  = {2013},

date = {2013-06-01},

journal = {Journal of Computational Neuroscience},

volume = {34},

number = {3},

pages = {411-432},

abstract = {A number of tests exist to check for statistical significance of phase synchronisation within the Electroencephalogram (EEG); however, the majority suffer from a lack of generality and applicability. They may also fail to account for temporal dynamics in the phase synchronisation, regarding synchronisation as a constant state instead of a dynamical process. Therefore, a novel test is developed for identifying the statistical significance of phase synchronisation based upon a combination of work characterising temporal dynamics of multivariate time-series and Markov modelling. We show how this method is better able to assess the significance of phase synchronisation than a range of commonly used significance tests. We also show how the method may be applied to identify and classify significantly different phase synchronisation dynamics in both univariate and multivariate datasets.},

keywords = {EEG, Functional connectivity, Markov models, Semi-Markov models, Significance testing},

pubstate = {published},

tppubtype = {article}

}

@article{Daly2013headArtifacts,

title = {On the automated removal of artifacts related to head movement from the EEG},

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

url = {http://www.iandaly.co.uk/newDesign2016/wp-content/uploads/2016/01/On-the-automated-removal-of-artifacts-related-to-head-movement-from-the-EEG.pdf},

doi = {10.1109/TNSRE.2013.2254724},

issn = {1534-4320},

year  = {2013},

date = {2013-05-01},

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

volume = {21},

number = {3},

pages = {427 - 434},

abstract = {Contamination of the electroencephalogram (EEG) by artifacts related to head movement is a major cause of reduced signal quality. This is a problem in both neuroscience and other uses of the EEG. To attempt to reduce the influence, on the EEG, of artifacts related to head movement, an accelerometer is placed on the head and independent component analysis is applied to attempt to separate artifacts which are statistically related to head movements. To evaluate the method, EEG and accelerometer measurements are made from 14 individuals with Cerebral palsy attempting to control a sensorimotor rhythm based brain-computer interface. Results show that the approach significantly reduces the influence of head movement related artifacts in the EEG.},

keywords = {Artefact removal, EEG, EMG, Head movement, ICA},

pubstate = {published},

tppubtype = {article}

}

@article{Chen2013a,

title = {Event-related desynchronization (ERD) in the alpha band during a hand mental rotation task},

author = {Xiaogang Chen and Guangyu Bin and Ian Daly and Xiaorong Gao},

url = {http://www.ncbi.nlm.nih.gov/pubmed/23458675},

doi = {10.1016/j.neulet.2013.02.036},

year  = {2013},

date = {2013-04-29},

journal = {Neuroscience Letters},

volume = {541},

pages = {238-242},

abstract = {Recent studies have demonstrated that mentally rotating the hands involves participants engaging in motor imagery processing. However, far less is known about the possible neurophysiological basis of such processing. To contribute to a better understanding of hand mental rotation processing, event-related spectral perturbation (ERSP) methods were applied to electroencephalography (EEG) data collected from participants mentally rotating their hands. Time-frequency analyses revealed that alpha-band power suppression was larger over central-parietal regions. This is in accordance with motor imagery findings suggesting that the motor regions may be involved in processing or detection of kinaesthetic information. Furthermore, the presence of a significant negative correlation between reaction times (RTs) and alpha-band power suppression over central regions is illustrated. These findings are consistent with the neural efficiency hypothesis, which proposes the non-use of many brain regions irrelevant for the task performance as well as the more focused use of specific task-related regions in individuals with better performance. These results indicate that ERSP provides some independent insights into the mental rotation process and further confirms that parietal and motor cortices are involved in mental rotation.},

keywords = {EEG, ERD, Hand rotation},

pubstate = {published},

tppubtype = {article}

}

@article{Pfurtscheller2012a,

title = {Coupling between intrinsic prefrontal HbO2 and central EEG beta power oscillations in the resting brain},

author = {Gert Pfurtscheller and Ian Daly and Gunther Bauernfeind and Gernot R. Muller-Putz},

url = {http://www.iandaly.co.uk/wp-content/uploads/2016/01/journal.pone_.0043640.pdf},

doi = {10.1371/journal.pone.0043640},

year  = {2012},

date = {2012-08-24},

journal = {PLOS One},

volume = {7},

number = {8},

pages = {1-9},

abstract = {There is increasing interest in the intrinsic activity in the resting brain, especially that of ultraslow and slow oscillations. Using near-infrared spectroscopy (NIRS), electroencephalography (EEG), blood pressure (BP), respiration and heart rate recordings during 5 minutes of rest, combined with cross spectral and sliding cross correlation calculations, we identified a short-lasting coupling (duration  100s) between prefrontal oxyhemoglobin (HbO2) in the frequency band between 0.07 and 0.13 Hz and central EEG alpha and/or beta power oscillations in 8 of the 9 subjects investigated. The HbO2 peaks preceded the EEG band power peaks by 3.7 s in 6 subjects, with moderate or no coupling between BP and HbO2 oscillations. HbO2 and EEG band power oscillations were approximately in phase with BP oscillations in the 2 subjects with an extremely high coupling (squared coherence >0.8) between BP and HbO2 oscillation. No coupling was identified in one subject. These results indicate that slow precentral (de)oxyhemoglobin concentration oscillations during awake rest can be temporarily coupled with EEG fluctuations in sensorimotor areas and modulate the excitability level in the brains’ motor areas, respectively. Therefore, this provides support for the idea that resting state networks fluctuate with frequencies of between 0.01 and 0.1 Hz (Mantini et.al. PNAS 2007).},

keywords = {Beta coupling, EEG, fNIRS, Resting state},

pubstate = {published},

tppubtype = {article}

}