Ant Colony System Optimization for Spatiotemporal Modelling of Combined EEG and MEG Data

Date

2021

Authors

Opoku, Eugene A.
Ahmed, Syed Ejaz
Song, Yin
Nathoo, Farouk S.

Journal Title

Journal ISSN

Volume Title

Publisher

Entropy

Abstract

Electroencephalography/Magnetoencephalography (EEG/MEG) source localization involves the estimation of neural activity inside the brain volume that underlies the EEG/MEG measures observed at the sensor array. In this paper, we consider a Bayesian finite spatial mixture model for source reconstruction and implement Ant Colony System (ACS) optimization coupled with Iterated Conditional Modes (ICM) for computing estimates of the neural source activity. Our approach is evaluated using simulation studies and a real data application in which we implement a nonparametric bootstrap for interval estimation. We demonstrate improved performance of the ACS-ICM algorithm as compared to existing methodology for the same spatiotemporal model.

Description

Keywords

ant colony system, bayesian spatial mixture model, inverse problem, nonparamtric boostrap, EEG/MEG data

Citation

Opoku, E. A., Ahmed, S. E., Song, Y., & Nathoo, F. S. (2021). Ant Colony System Optimization for Spatiotemporal Modelling of Combined EEG and MEG Data. Entropy, 23(3), 1-35. https://doi.org/10.3390/e23030329.