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Assessment of Epileptic Gamma Oscillations’ Networks Connectivity

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Intelligent Systems Design and Applications (ISDA 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 716))

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Abstract

Source localization consists in defining exact position of the brain generators for a time course obtained from a surface electrophysiological signal (EEG, MEG), in order to determine with a high precision the epileptogenic zones. We applied diverse inverse problem techniques to obtain this resolution. These techniques present various hypotheses and specific epileptic network connectivity. We proposed here to rate the performance of issued inverse problem in identifying epileptic zone. Then, we used four methods of inverse problem to explain cortical areas and neural generators of excessive discharges. We computed network connectivity of each technique. We applied a pre processing chain to assess the rate of epileptic gamma oscillation connectivity among MEG of each technique. Wavelet Maximum Entropy on the Mean (wMEM) proved a high matching between MEG network connectivity based on Correlation, Coherence, Granger Causality (GC) and Phase Locking Value (PLV) between active sources, followed by Dynamical Statistical Parametric Mapping (dSPM), standardized low-resolution brain electromagnetic tomography (sLORETA), and Minimum norm estimation (MNE). The problem techniques studied are at least able to find theoretically part of seizure onset zone. wMEM and dSPM represent the most powerful connection of all techniques.

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Acknowledgment

This work was supported by 20PJEC0613 “Hatem Ben Taher Tunisian Project”.

Author information

Authors and Affiliations

  1. Miracl Lab, Sfax University, Sfax, Tunisia

    Amal Necibi & Nawel Jmail

  2. Regim Laboratory, ENIS, Sfax University, Sfax, Tunisia

    Abir Hadriche

Authors
  1. Amal Necibi
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  2. Abir Hadriche
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  3. Nawel Jmail
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Corresponding author

Correspondence to Amal Necibi .

Editor information

Editors and Affiliations

  1. Faculty of Computing and Data Science, FLAME University, Pune, Maharashtra, India

    Ajith Abraham

  2. Center for Smart Computing Continuum, Burgenland, Austria

    Sabri Pllana

  3. University of Bari, Bari, Italy

    Gabriella Casalino

  4. University of Jinan, Jinan, Shandong, China

    Kun Ma

  5. Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, India

    Anu Bajaj

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Necibi, A., Hadriche, A., Jmail, N. (2023). Assessment of Epileptic Gamma Oscillations’ Networks Connectivity. In: Abraham, A., Pllana, S., Casalino, G., Ma, K., Bajaj, A. (eds) Intelligent Systems Design and Applications. ISDA 2022. Lecture Notes in Networks and Systems, vol 716. Springer, Cham. https://doi.org/10.1007/978-3-031-35501-1_9

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