Imperial College London

DrNicolettaNicolaou

Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Honorary Research Fellow
 
 
 
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Contact

 

+44 (0)20 7594 0875n.nicolaou Website

 
 
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Location

 

B422Bessemer BuildingSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
to

40 results found

Nicolaou N, Malik A, Daly I, Weaver J, Hwang F, Kirke A, Roesch EB, Williams D, Miranda ER, Nasuto SJet al., 2017, Directed Motor-Auditory EEG Connectivity Is Modulated by Music Tempo, FRONTIERS IN HUMAN NEUROSCIENCE, Vol: 11, ISSN: 1662-5161

Journal article

Lauteslager T, Nicolaou N, Lande TS, Constandinou TGet al., 2016, Functional neuroimaging Using UWB Impulse Radar: a Feasibility Study, IEEE Biomedical Circuits and Systems (BioCAS) Conference, Publisher: IEEE, Pages: 406-409

Microwave imaging is a promising new modalityfor studying brain function. In the current paper we assess thefeasibility of using a single chip implementation of an ultra-wideband impulse radar for developing a portable and low-costfunctional neuroimaging device. A numerical model is used topredict the level of attenuation that will occur when detectinga volume of blood in the cerebral cortex. A phantom liquid ismade, to study the radar’s performance at different attenuationlevels. Although the radar is currently capable of detecting apoint reflector in a phantom liquid with submillimeter accuracyand high temporal resolution, object detection at the desired levelof attenuation remains a challenge.

Conference paper

Nicolaou N, Constandinou TG, 2016, A nonlinear causality estimator based on Non-Parametric Multiplicative Regression, Frontiers in Neuroinformatics, Vol: 10, ISSN: 1662-5196

Causal prediction has become a popular tool for neuroscience applications, as it allows the study of relationships between different brain areas during rest, cognitive tasks or brain disorders. We propose a nonparametric approach for the estimation of nonlinear causal prediction for multivariate time series. In the proposed estimator, C-NPMR, Autoregressive modelling is replaced by Nonparametric Multiplicative Regression (NPMR). NPMR quantifies interactions between a response variable (effect) and a set of predictor variables (cause); here, we modified NPMR for model prediction. We also demonstrate how a particular measure, the sensitivity Q, could be used to reveal the structure of the underlying causal relationships. We apply C-NPMR on artificial data with known ground truth (5 datasets), as well as physiological data (2 datasets). C-NPMR correctly identifies both linear and nonlinear causal connections that are present in the artificial data, as well as physiologically relevant connectivity in the real data, and does not seem to be affected by filtering. The Sensitivity measure also provides useful information about the latent connectivity.The proposed estimator addresses many of the limitations of linear Granger causality and other nonlinear causality estimators. C-NPMR is compared with pairwise and conditional Granger causality (linear) and Kernel-Granger causality (nonlinear). The proposed estimator can be applied to pairwise or multivariate estimations without any modifications to the main method. Its nonpametric nature, its ability to capture nonlinear relationships and its robustness to filtering make it appealing for a number of applications.

Journal article

Demarchou E, Georgiou J, Nicolaou N, Constandinou TGet al., 2014, Anesthetic-induced changes in EEG activity: a graph theoretical approach, IEEE Biomedical Circuits and Systems (BioCAS) Conference, Pages: 45-48

The dynamic brain networks forming during wakefulness and anesthetic-induced unconsciousness are investigated using time-delayed correlation and graph theoretical measures. Electrical brain activity (EEG) from 10 patients under propofol anesthesia during routine surgery is characterized using the shortest path length, λ, and clustering, c, extracted from time delayed correlation. An increase in λ and c during anesthesiareveals disruption of long-range connections and emergence of more localized neighborhoods. These changes were not a result of volume conduction, as were based on time-delayed correlation. Our observations are in line with theories of anesthetic action and support the use of graph theoretic measures to study emerging brain networks during wakefulness and anesthesia.

Conference paper

Nicolaou N, Georgiou J, 2014, The Study of EEG Dynamics During Anesthesia with Cross-Recurrence Rate, Cureus, Vol: 6

Journal article

Nicolaou N, Georgiou J, 2014, Neural Network-Based Classification of Anesthesia/Awareness Using Granger Causality Features, CLINICAL EEG AND NEUROSCIENCE, Vol: 45, Pages: 77-88, ISSN: 1550-0594

Journal article

Nicolaou N, Georgiou J, 2014, Global field synchrony during general anaesthesia, BRITISH JOURNAL OF ANAESTHESIA, Vol: 112, Pages: 529-539, ISSN: 0007-0912

Journal article

Nicolaou N, Georgiou J, 2014, Spatial Analytic Phase Difference of EEG activity during anesthetic-induced unconsciousness, Clinical Neurophysiology

Journal article

Nicolaou N, Georgiou J, 2013, Towards automatic sleep staging via Cross-Recurrence Rate of EEG and ECG activity, IEEE BioCAS, Pages: 198-201

Conference paper

Daly I, Nicolaou N, Nasuto SJ, Warwick Ket al., 2013, Automated Artifact Removal From the Electroencephalogram: A Comparative Study, CLINICAL EEG AND NEUROSCIENCE, Vol: 44, Pages: 291-306, ISSN: 1550-0594

Journal article

Nicolaou N, Georgiou J, 2013, Monitoring depth of hypnosis under propofol general anaesthesia: Granger Causality and Hidden Markov Models, Neurotechnix (special session: BrainRehab)

Conference paper

Garreau G, Nicolaou N, Georgiou J, 2012, Individual classification through autoregressive modelling of micro-Doppler signatures, IEEE BioCAS, Pages: 312-315

Conference paper

Nicolaou N, Dionysiou A, Georgiou J, 2012, Temporal dynamics of EEG during anesthesia, 12th IEEE BIBE, Pages: 288-291

Conference paper

Nicolaou N, Hourris S, Alexandrou P, Georgiou Jet al., 2012, EEG-Based Automatic Classification of 'Awake' versus 'Anesthetized' State in General Anesthesia Using Granger Causality, PLOS ONE, Vol: 7, ISSN: 1932-6203

Journal article

Nicolaou N, Hourris S, Alexandrou P, Georgiou Jet al., 2012, Permutation entropy: A reliable measure for automatic monitoring of anesthetic depth during surgery?, Engineering Intelligent Systems, Vol: 20, Pages: 9-18, ISSN: 1472-8915

Permutation Entropy (PE) has recently been applied to characterize anesthetic-induced changes in the frontal electrical brain activity (EEG) during anesthesia. In this work we investigate the stability of PE as a means of identifying between the awake and anesthetized EEG over the entire duration of surgery under different anesthetic regimes and using a full set of EEG sensors. Average classification rates from 22 patients range between 98-99% (specificity, sensitivity and accuracy), when using information from whole-head EEG. The findings support the robustness of PE for discriminating 'awake' and 'anesthesia' throughout the entire surgery, independently of the anesthetic regime followed. ©2012 CRL Publishing Ltd.

Journal article

Nicolaou N, Georgiou J, 2012, Detection of epileptic electroencephalogram based on Permutation Entropy and Support Vector Machines, EXPERT SYSTEMS WITH APPLICATIONS, Vol: 39, Pages: 202-209, ISSN: 0957-4174

Journal article

Nicolaou N, Houris S, Alexandrou P, Georgiou Jet al., 2012, Permutation Entropy: a reliable measure for automatic monitoring of anesthetic depth during surgery?, Engineering Intelligent Systems Journal (Special Issue: Timely developments in Artificial Intelligence Applications), Vol: 20, Pages: 1-10

Journal article

Nicolaou N, Houris S, Alexandrou P, Georgiou Jet al., 2011, Cross-Recurrence Rate for discriminating 'conscious' and 'unconscious' state in propofol general anesthesia, IEEE BioCAS, Pages: 416-419

Conference paper

Nicolaou N, Houris S, Alexandrou P, Georgiou Jet al., 2011, Permutation Entropy for discriminating ‘conscious’ and ‘unconscious’ state in general anaesthesia, Engineering Applications of Neural Networks, Editors: Iliadis, Jayne, Publisher: Springer Boston, Pages: 280-288

Book chapter

Garreau G, Nicolaou N, Andreou C, D'Urball C, Stuarts G, Georgiou Jet al., 2011, Computationally efficient classification of human transport mode using micro-Doppler signatures, 45th CISS

Conference paper

Nicolaou N, Houris S, Alexandrou P, Georgiou Jet al., 2011, Using Granger Causality to characterise bidirectional interactions in the human brain during induction of anaesthesia, BIOSIGNALS

Conference paper

Nicolaou N, Georgiou J, 2011, The Use of Permutation Entropy to Characterize Sleep Electroencephalograms, CLINICAL EEG AND NEUROSCIENCE, Vol: 42, Pages: 24-28, ISSN: 1550-0594

Journal article

Nicolaou N, Houris S, Alexandrou P, Georgiou Jet al., 2011, Entropy Measures for Discrimination of 'awake' Vs 'anaesthetized' State in Recovery from General Anesthesia, 33rd Annual International Conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBS), Publisher: IEEE, Pages: 2598-2601, ISSN: 1557-170X

Conference paper

Demosthenous P, Nicolaou N, Georgiou J, 2010, A Hardware-efficient Lowpass filter design for biomedical applications, IEEE BioCAS, Pages: 130-133

Conference paper

Nicolaou N, Georgiou J, 2010, Permutation Entropy: a new feature for Brain-Computer Interfaces, IEEE BioCAS, Pages: 49-52

Conference paper

Nicolaou N, Georgiou J, 2009, Autoregressive model order estimation criteria for monitoring awareness during anaesthesia, Proceedings of the 5th IFIP Conference on Artificial Intelligence Applications and Innovations (AIAI'2009), Editors: Papadopoulos, Andreou, Iliadis, Maglogiannis, Publisher: Springer Berlin Heideberg, Pages: 71-80

Book chapter

Nicolaou N, Georgiou J, 2009, Towards a Morse Code-Based Non-invasive Though-to-Speech Converter, Biomedical Engineering Systems and Technologies, Editors: Fred, Filipe, Gamboa, Publisher: Springer-Verlag Berlin Heidelberg, Pages: 123-135

Book chapter

Petroudi S, Nicolaou N, Georgiou J, Brady Met al., 2008, Breast abnormality detection incorporating breast density information based on Independent Component Analysis, IWDM, Pages: 667-673

Conference paper

Nicolaou N, Petroudi S, Georgiou J, Polycarpou M, Brady Met al., 2008, Digital mammography: towards pectoral muscle removal via Independent Component Analysis, IEE MEDSIP

Conference paper

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