A
Ahmad Mheich
Researcher at University of Rennes
Publications - 26
Citations - 440
Ahmad Mheich is an academic researcher from University of Rennes. The author has contributed to research in topics: Electroencephalography & Similarity (network science). The author has an hindex of 10, co-authored 24 publications receiving 317 citations. Previous affiliations of Ahmad Mheich include French Institute of Health and Medical Research & Lebanese University.
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Journal ArticleDOI
Identification of Interictal Epileptic Networks from Dense-EEG.
Mahmoud Hassan,Isabelle Merlet,Isabelle Merlet,Ahmad Mheich,Ahmad Mheich,Ahmad Mheich,Aya Kabbara,Aya Kabbara,Aya Kabbara,Arnaud Biraben,Arnaud Biraben,Anca Nica,Fabrice Wendling,Fabrice Wendling +13 more
TL;DR: Results suggest that source connectivity method, when appropriately configured, is able to extract highly relevant diagnostic information about networks involved in interictal epileptic spikes from non-invasive dense-EEG data.
Journal ArticleDOI
A new algorithm for spatiotemporal analysis of brain functional connectivity.
TL;DR: A new algorithm to track the functional brain connectivity dynamics is proposed based on the K-means clustering of the connectivity graphs obtained from the phase locking value (PLV) method applied on hr-EEG, which aims at segmenting high-resolution EEG signals into functional connectivity microstates during a picture naming task.
Posted Content
Identification of interictal epileptic networks from dense-EEG
Mahmoud Hassan,Isabelle Merlet,Isabelle Merlet,Ahmad Mheich,Ahmad Mheich,Ahmad Mheich,Aya Kabbara,Aya Kabbara,Aya Kabbara,Arnaud Biraben,Arnaud Biraben,Anca Nica,Fabrice Wendling,Fabrice Wendling +13 more
TL;DR: In this article, the effect of the two key factors involved in EEG source connectivity processing are analyzed: (1) the algorithm used in the solution of the EEG inverse problem and (2) the estimation of the functional connectivity.
Journal ArticleDOI
Decreased integration of EEG source-space networks in disorders of consciousness.
Jennifer Rizkallah,Jitka Annen,Julien Modolo,Olivia Gosseries,Pascal Benquet,Sepehr Mortaheb,Hassan Amoud,Helena Cassol,Ahmad Mheich,Aurore Thibaut,Camille Chatelle,Mahmoud Hassan,Rajanikant Panda,Fabrice Wendling,Steven Laureys +14 more
TL;DR: High-density electroencephalography data showed that networks in DOC patients are characterized by impaired global information processing and increased local information processing (network segregation) as compared to controls and the large-scale functional brain networks had integration decreasing with lower level of consciousness.
Journal ArticleDOI
SimiNet: A Novel Method for Quantifying Brain Network Similarity
TL;DR: A novel algorithm called SimiNet for measuring similarity between two graphs whose nodes are defined a priori within a 3D coordinate system that shows high performance to detect spatial variation of brain networks obtained during a naming task of two categories of visual stimuli: animals and tools.