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EEG Source Imaging by Supervised Learning

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اقرأ أكثر حفظ في قائمتي
  • معلومة اضافية
    • Contributors:
      Département lmage et Traitement Information (IMT Atlantique - ITI); IMT Atlantique (IMT Atlantique); Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT); Laboratoire de Traitement de l'Information Medicale (LaTIM); Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-IMT Atlantique (IMT Atlantique); Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT)-Institut Brestois Santé Agro Matière (IBSAM); Université de Brest (UBO); Equipe PIM (Lab-STICC_PIM); Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC); École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom Paris (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique); Institut Mines-Télécom Paris (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom Paris (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique); Institut Mines-Télécom Paris (IMT); Département Micro-Ondes (IMT Atlantique - MO); CHU Brest; ANR-19-CHIA-0015,AI-4-CHILD,IA au service de la neuroréhabilitation pédiatrique(2019); ANR-10-LABX-0007,COMIN Labs,Digital Communication and Information Sciences for the Future Internet(2010)
    • بيانات النشر:
      HAL CCSD
      IEEE
    • الموضوع:
      2023
    • Collection:
      Université de Bretagne Occidentale: HAL
    • الموضوع:
      Helsinki; Finland
    • نبذة مختصرة :
      International audience ; Estimating the electrophysiological activity at the origin of electroencephalography (EEG) measurements is an ill-posed inverse problem. Several methods solve this problem by imposing different priors on the solution. Machine learning could allow to learn the inverse function directly from the data and thusmake the choice of one of the multiple solutions of the inverse problem more reliable. This work is based on simulations of electrophysiologic data containing single or multiple extended sources, using the SEREEGA simulation toolbox [1]. These data are used to train a one-dimensional convolutional network (1D-CNN) and to compare the results of this learning approach to those obtained by a recurrent long short term memory (LSTM) network from the literature, and by minimum norm energy [2] (MNE) and standardized low resolution brain electromagnetic tomography [3] (sLORETA) methods. These results on simulated data are encouraging about the potential contribution of learning-based methods to the problem of spatio-temporal EEG sourceimaging. Additional work still needs to be done in order to also evaluate the ability of the network to generalize to real data.
    • Relation:
      hal-04591918; https://hal.science/hal-04591918; https://hal.science/hal-04591918/document; https://hal.science/hal-04591918/file/EUSIPCO_2023_reynaud_vfinale.pdf
    • الرقم المعرف:
      10.23919/EUSIPCO58844.2023.10290011
    • Rights:
      info:eu-repo/semantics/OpenAccess
    • الرقم المعرف:
      edsbas.5E0E11F0