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Bilal H. Fadlallah
Researcher at University of Florida
Publications - 15
Citations - 493
Bilal H. Fadlallah is an academic researcher from University of Florida. The author has contributed to research in topics: Fusiform gyrus & Conditional entropy. The author has an hindex of 8, co-authored 15 publications receiving 414 citations. Previous affiliations of Bilal H. Fadlallah include Georgia Institute of Technology.
Papers
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Journal ArticleDOI
Weighted-permutation entropy: a complexity measure for time series incorporating amplitude information.
TL;DR: This paper proposes a simple method to address PE's limitations, mainly its inability to differentiate between distinct patterns of a certain motif and the sensitivity of patterns close to the noise floor, by assigning weights for each extracted vector when computing the relative frequencies associated with every motif.
Journal ArticleDOI
Systems biology, bioinformatics, and biomarkers in neuropsychiatry.
Ali Alawieh,Fadi A. Zaraket,Jian-Liang Li,Stefania Mondello,Amaly Nokkari,Mahdi Razafsha,Bilal H. Fadlallah,Rose-Mary Boustany,Firas Kobeissy,Firas Kobeissy +9 more
TL;DR: Light is shed on the need of systems biology, bioinformatics, and biomarkers in neuropsychiatry, and how the knowledge gained through these methodologies can be translated into clinical use providing clinicians with improved ability to diagnose, manage, and treat NP patients is illustrated.
Journal ArticleDOI
Quantifying Cognitive State From EEG Using Dependence Measures
TL;DR: This study hypothesizes and demonstrates that it is possible to automatically discriminate face processing from processing of a simple control stimulus based on processed EEGs in an online fashion with high temporal resolution using measures of statistical dependence applied on steady-state visual evoked potentials.
Proceedings ArticleDOI
An Association Framework to Analyze Dependence Structure in Time Series
TL;DR: A modification to the generalized measure of association framework that reduces the effect of temporal structure in time series and can capture pairwise dependence between generated signals as well as their envelopes with good statistical power is proposed.