40 results found
Wu S-J, Nicolaou N, Bogdan M, 2020, Consciousness Detection in a Complete Locked-in Syndrome Patient through Multiscale Approach Analysis, ENTROPY, Vol: 22
Nicolaou N, Malik A, Daly I, et al., 2017, Directed Motor-Auditory EEG Connectivity Is Modulated by Music Tempo, FRONTIERS IN HUMAN NEUROSCIENCE, Vol: 11, ISSN: 1662-5161
Lauteslager T, Nicolaou N, Lande TS, et 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.
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.
Demarchou E, Georgiou J, Nicolaou N, et 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.
Nicolaou N, Georgiou J, 2014, The Study of EEG Dynamics During Anesthesia with Cross-Recurrence Rate, Cureus, Vol: 6
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
Nicolaou N, Georgiou J, 2014, Global field synchrony during general anaesthesia, BRITISH JOURNAL OF ANAESTHESIA, Vol: 112, Pages: 529-539, ISSN: 0007-0912
Nicolaou N, Georgiou J, 2014, Spatial Analytic Phase Difference of EEG activity during anesthetic-induced unconsciousness, Clinical Neurophysiology
Nicolaou N, Georgiou J, 2013, Towards automatic sleep staging via Cross-Recurrence Rate of EEG and ECG activity, IEEE BioCAS, Pages: 198-201
Daly I, Nicolaou N, Nasuto SJ, et al., 2013, Automated Artifact Removal From the Electroencephalogram: A Comparative Study, CLINICAL EEG AND NEUROSCIENCE, Vol: 44, Pages: 291-306, ISSN: 1550-0594
Nicolaou N, Georgiou J, 2013, Monitoring depth of hypnosis under propofol general anaesthesia: Granger Causality and Hidden Markov Models, Neurotechnix (special session: BrainRehab)
Garreau G, Nicolaou N, Georgiou J, 2012, Individual classification through autoregressive modelling of micro-Doppler signatures, IEEE BioCAS, Pages: 312-315
Nicolaou N, Dionysiou A, Georgiou J, 2012, Temporal dynamics of EEG during anesthesia, 12th IEEE BIBE, Pages: 288-291
Nicolaou N, Hourris S, Alexandrou P, et al., 2012, EEG-Based Automatic Classification of 'Awake' versus 'Anesthetized' State in General Anesthesia Using Granger Causality, PLOS ONE, Vol: 7, ISSN: 1932-6203
Nicolaou N, Hourris S, Alexandrou P, et 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.
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
Nicolaou N, Houris S, Alexandrou P, et 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
Nicolaou N, Houris S, Alexandrou P, et al., 2011, Cross-Recurrence Rate for discriminating 'conscious' and 'unconscious' state in propofol general anesthesia, IEEE BioCAS, Pages: 416-419
Nicolaou N, Houris S, Alexandrou P, et 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
Garreau G, Nicolaou N, Andreou C, et al., 2011, Computationally efficient classification of human transport mode using micro-Doppler signatures, 45th CISS
Nicolaou N, Houris S, Alexandrou P, et al., 2011, Using Granger Causality to characterise bidirectional interactions in the human brain during induction of anaesthesia, BIOSIGNALS
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
Nicolaou N, Houris S, Alexandrou P, et 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
Demosthenous P, Nicolaou N, Georgiou J, 2010, A Hardware-efficient Lowpass filter design for biomedical applications, IEEE BioCAS, Pages: 130-133
Nicolaou N, Georgiou J, 2010, Permutation Entropy: a new feature for Brain-Computer Interfaces, IEEE BioCAS, Pages: 49-52
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
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
Petroudi S, Nicolaou N, Georgiou J, et al., 2008, Breast abnormality detection incorporating breast density information based on Independent Component Analysis, IWDM, Pages: 667-673
Nicolaou N, Petroudi S, Georgiou J, et al., 2008, Digital mammography: towards pectoral muscle removal via Independent Component Analysis, IEE MEDSIP
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