Charles-Francois Vincent Latchoumane

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Methods for the extraction of features from physiological datasets are growing needs as clinical investigations of Alzheimer's disease (AD) in large and heterogeneous population increase. General tools allowing diagnostic regardless of recording sites, such as different hospitals, are essential and if combined to inexpensive non-invasive methods could(More)
AIM While volumetric and metabolic imaging on post-traumatic stress disorder (PTSD) patients has been intensively performed, few studies using electroencephalograms (EEG) have been done as yet. The aim of the present study was to investigate abnormalities in functional connectivity of cortical networks in PTSD. METHODS Non-linear interdependence (NI), a(More)
Highlights > Multi-Array Decomposition (MAD) method to extract unique, general features for multi-site diagnosis of Alzheimer's disease. > Two state-of-the-art MAD were used: PARAFAC and Non-Negative Tensor decomposition. > MAD outperformed spectral mean, spatial-spectral mean, AMUSE and SVD/unfolding in training (97.6%), validation (97.6%), regularization(More)
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