144 research outputs found
Classification of Epileptic EEG Signals by Wavelet based CFC
Electroencephalogram, an influential equipment for analyzing humans
activities and recognition of seizure attacks can play a crucial role in
designing accurate systems which can distinguish ictal seizures from regular
brain alertness, since it is the first step towards accomplishing a high
accuracy computer aided diagnosis system (CAD). In this article a novel
approach for classification of ictal signals with wavelet based cross frequency
coupling (CFC) is suggested. After extracting features by wavelet based CFC,
optimal features have been selected by t-test and quadratic discriminant
analysis (QDA) have completed the Classification.Comment: Electroencephalogram; Wavelet Decomposition; Cross Frequency
Coupling;Quadratic Discriminant Analysis; T-test Feature Selectio
Extended Recurrence Plot Analysis and its Application to ERP Data
We present new measures of complexity and their application to event related
potential data. The new measures base on structures of recurrence plots and
makes the identification of chaos-chaos transitions possible. The application
of these measures to data from single-trials of the Oddball experiment can
identify laminar states therein. This offers a new way of analyzing
event-related activity on a single-trial basis.Comment: 21 pages, 8 figures; article for the workshop ''Analyzing and
Modelling Event-Related Brain Potentials: Cognitive and Neural Approaches``
at November 29 - December 01, 2001 in Potsdam, German
Ordinal patterns in epileptic brains: Analysis of intracranial EEG and simultaneous EEG-fMRI
Epileptic seizures are associated with high behavioral stereotypy of the patients. In the EEG of epilepsy patients characteristic signal patterns can be found during and between seizures. Here we use ordinal patterns to analyze EEGs of epilepsy patients and quantify the degree of signal determinism. Besides relative signal redundancy and the fraction of forbidden patterns we introduce the fraction of under-represented patterns as a new measure. Using the logistic map, parameter scans are performed to explore the sensitivity of the measures to signal determinism. Thereafter, application is made to two types of EEGs recorded in two epilepsy patients. Intracranial EEG shows pronounced determinism peaks during seizures. Finally, we demonstrate that ordinal patterns may be useful for improving analysis of non-invasive simultaneous EEG-fMR
Ictal time-irreversible intracranial EEG signals as markers of the epileptogenic zone.
OBJECTIVE: To show that time-irreversible EEG signals recorded with intracranial electrodes during seizures can serve as markers of the epileptogenic zone. METHODS: We use the recently developed method of mapping time series into directed horizontal graphs (dHVG). Each node of the dHVG represents a time point in the original intracranial EEG (iEEG) signal. Statistically significant differences between the distributions of the nodes' number of input and output connections are used to detect time-irreversible iEEG signals. RESULTS: In 31 of 32 seizure recordings we found time-irreversible iEEG signals. The maximally time-irreversible signals always occurred during seizures, with highest probability in the middle of the first seizure half. These signals spanned a large range of frequencies and amplitudes but were all characterized by saw-tooth like shaped components. Brain regions removed from patients who became post-surgically seizure-free generated significantly larger time-irreversibilities than regions removed from patients who still had seizures after surgery. CONCLUSIONS: Our results corroborate that ictal time-irreversible iEEG signals can indeed serve as markers of the epileptogenic zone and can be efficiently detected and quantified in a time-resolved manner by dHVG based methods. SIGNIFICANCE: Ictal time-irreversible EEG signals can help to improve pre-surgical evaluation in patients suffering from pharmaco-resistant epilepsies.K.S. gratefully acknowledges support by the Swiss National Science Foundation (SNF
32003B_155950). H.G. gratefully acknowledges support by a Research Grant of the
Inselspital Bern. R.G.A. acknowledges funding from the Volkswagen foundation and was
supported by the Spanish Ministry of Economy and Competitiveness (Grant FIS2014-54177-
R). This project has received funding from the European Union’s Horizon 2020 research and
innovation programme under the Marie Sklodowska-Curie grant agreement No 642563
(R.G.A.). MG gratefully acknowledges the financial support of the EPSRC via grant
EP/N014391/1, funding from Epilepsy Research UK via grant number A1007 and was
generously supported by a Wellcome Trust Institutional Strategic Support Award
(WT105618MA)
HERMES: Towards an Integrated Toolbox to Characterize Functional and Effective Brain Connectivity
The analysis of the interdependence between time series has become an important field of research in the last years, mainly as a result of advances in the characterization of dynamical systems from the signals they produce, the introduction of concepts such as generalized and phase synchronization and the application of information theory to time series analysis. In neurophysiology, different analytical tools stemming from these concepts have added to the ‘traditional’ set of linear methods, which includes the cross-correlation and the coherency function in the time and frequency domain, respectively, or more elaborated tools such as Granger Causality. This increase in the number of approaches to tackle the existence of functional (FC) or effective connectivity (EC) between two (or among many) neural networks, along with the mathematical complexity of the corresponding time series analysis tools, makes it desirable to arrange them into a unified-easy-to-use software package. The goal is to allow neuroscientists, neurophysiologists and researchers from related fields to easily access and make use of these analysis methods from a single integrated toolbox. Here we present HERMES (http://hermes.ctb.upm.es), a toolbox for the Matlab® environment (The Mathworks, Inc), which is designed to study functional and effective brain connectivity from neurophysiological data such as multivariate EEG and/or MEG records. It includes also visualization tools and statistical methods to address the problem of multiple comparisons. We believe that this toolbox will be very helpful to all the researchers working in the emerging field of brain connectivity analysis
Seizure prediction : ready for a new era
Acknowledgements: The authors acknowledge colleagues in the international seizure prediction group for valuable discussions. L.K. acknowledges funding support from the National Health and Medical Research Council (APP1130468) and the James S. McDonnell Foundation (220020419) and acknowledges the contribution of Dean R. Freestone at the University of Melbourne, Australia, to the creation of Fig. 3.Peer reviewedPostprin
BrainNetVis: An Open-Access Tool to Effectively Quantify and Visualize Brain Networks
This paper presents BrainNetVis, a tool which serves brain network modelling
and visualization, by providing both quantitative and qualitative network measures
of brain interconnectivity. It emphasizes the needs that led to the creation of this
tool by presenting similar works in the field and by describing how our tool contributes
to the existing scenery. It also describes the methods used for the calculation
of the graph metrics (global network metrics and vertex metrics), which carry
the brain network information. To make the methods clear and understandable, we
use an exemplar dataset throughout the paper, on which the calculations and the
visualizations are performed. This dataset consists of an alcoholic and a control
group of subjects
- …