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EPINET

Detection of brain patterns for the characterisation of epileptic networks

Total Cost €

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EC-Contrib. €

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Partnership

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Project "EPINET" data sheet

The following table provides information about the project.

Coordinator
ASTON UNIVERSITY 

Organization address
address: ASTON TRIANGLE
city: BIRMINGHAM
postcode: B4 7ET
website: www.aston.ac.uk

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country United Kingdom [UK]
 Project website https://github.com/quitadal/EPINETLAB
 Total cost 195˙454 €
 EC max contribution 195˙454 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2014
 Funding Scheme MSCA-IF-EF-ST
 Starting year 2015
 Duration (year-month-day) from 2015-10-05   to  2017-10-04

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    ASTON UNIVERSITY UK (BIRMINGHAM) coordinator 195˙454.00

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 Project objective

This project will bring in a research fellow with significant experience in the development and application of signal processing tools applied to electromagnetic brain signal, to work with a group carrying out leading research in epilepsy and in the development of non-invasive tests to localise brain function in patients with drug-resistant epilepsy. EPINET research aims at developing and validating innovative methods to localise and characterise non-invasively functional properties of the epileptogenic zone (EZ), i.e. the area responsible for the generation of epileptic seizures. The candidate, L. Quitadamo (LQ), will bring to the team expertise in the classification of biological signal and computer programming developed through collaborations with world-leading teams involved in brain-computer interface (BCI) research, complementing the expertise of the host research group in non-invasive mapping of brain function. Furthermore, she will benefit from expert training in the analysis of neuroimaging and neurophysiological tests (Magnetoencephalography (MEG), High Resolution EEG (HR-EEG), spike-activated fMRI and Intracranial EEG). The benefit will be two-fold: The candidate will extend her expertise in signal processing to the clinical assessment of patients with drug-resistant epilepsy, developing new and complementary skills applied to specific clinical applications, which will enable her to reach a position of maturity and professional independence. The host group will have access to new methods of classification of bioelectrical signal and develop analysis tools that will be made available in the public domain together with a repository of multimodal electromagnetic signal obtained in the presurgical assessment of patients with epilepsy.

 Publications

year authors and title journal last update
List of publications.
2017 L R Quitadamo, F Cavrini, L Sbernini, F Riillo, L Bianchi, S Seri, G Saggio
Support vector machines to detect physiological patterns for EEG and EMG-based human–computer interaction: a review
published pages: 11001, ISSN: 1741-2560, DOI: 10.1088/1741-2552/14/1/011001
Journal of Neural Engineering 14/1 2019-06-13
2017 LUCIA RITA QUITADAMO, ROBERTO MAI, FRANCESCA GOZZO, VERONICA PELLICCIA, FRANCESCO CARDINALE, STEFANO SERI
KURTOSIS-BASED DETECTION OF INTRACRANIAL HIGH-FREQUENCY OSCILLATIONS FOR THE IDENTIFICATION OF THE SEIZURE ONSET ZONE
published pages: , ISSN: 0129-0657, DOI:
International Journal of Neural Systems 2019-06-13

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The information about "EPINET" are provided by the European Opendata Portal: CORDIS opendata.

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