Design of MRI Structured Spiking Neural Networks and Learning Algorithms for Personalized Modelling, Analysis, and Prediction of EEG Signals

Samaneh Alsadat Saeedinia, Mohammad Reza Jahed-Motlagh, Abbas Tafakhori, Nikola Kasabov

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Abstract

This paper proposes a novel method and algorithms for the design of MRI structured personalized 3D spiking neural network models (MRI-SNN) for a better analysis, modeling, and prediction of EEG signals. It proposes a novel gradient-descent learning algorithm integrated with a spike-time-dependent-plasticity algorithm. The models capture informative personal patterns of interaction between EEG channels, contrary to single EEG signal modeling methods or to spike-based approaches which do not use personal MRI data to pre-structure a model. The proposed models can not only learn and model accurately measured EEG data, but they can also predict signals at 3D model locations that correspond to non-monitored brain areas, e.g. other EEG channels, from where data has not been collected. This is the first study in this respect. As an illustration of the method, personalized MRI-SNN models are created and tested on EEG data from two subjects. The models result in better prediction accuracy and a better understanding of the personalized EEG signals than traditional methods due to the MRI and EEG information integration. The models are interpretable and facilitate a better understanding of related brain processes. This approach can be applied for personalized modeling, analysis, and prediction of EEG signals across brain studies such as the study and prediction of epilepsy, peri-perceptual brain activities, brain-computer interfaces, and others.
Original languageEnglish
Article number12064 (2021)
Pages (from-to)1-20
Number of pages20
JournalScientific Reports
Volume11
DOIs
Publication statusPublished - 8 Jun 2021

Keywords

  • spiking neural networks; epilepsy; NeuCube
  • personalized modeling
  • EEG
  • MRI
  • Spike-time dependent plasticity
  • gradient decent algorithm
  • biological signal prediction
  • Computational Models
  • Computational Neuroscience
  • Data Integration
  • Image Processing

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