Effective Brain Connectivity for fNIRS with Fuzzy Cognitive Maps in Neuroergonomics
Date
2022Language
en
Sujet
Résumé
Effective connectivity (EC) among functional near-infrared spectroscopy (fNIRS) signals is a quantitative measure of the strength of influence between brain activity associated with different regions of the brain. Evidently, accurate deciphering of EC gives further insight into the understanding of the intricately complex nature of neuronal interactions in the human brain. This article presents a novel approach to estimate EC in the human brain signals using enhanced fuzzy cognitive maps (FCMs). The proposed method presents a regularized methodology of FCMs, called effective FCMs (E-FCMs), with improved accuracy for predicting EC between real and synthetic fNIRS signals. Essentially, the revisions made in the FCM methodology include a more powerful prediction formula for FCM combined with independent tuning of the transformation function parameter. A comparison of EC in fNIRS signals obtained from E-FCM with that obtained from standard FCM, general linear model (GLM) parameters that power dynamic causal modeling (DCM), and Granger causality (GC) manifests the greater prowess of the proposed E-FCM over the aforementioned methods. For real fNIRS data, an empirical investigation is also made to gain an insight into the role of oxyhemoglobin and deoxy-hemoglobin (oxy-Hb, deoxy-Hb) in representing the cognitive activity. We believe this article has profound implications for neuroergonomics research communities. © 2016 IEEE.
Collections
Related items
Showing items related by title, author, creator and subject.
-
Constructive Fuzzy Cognitive Map for Depression Severity Estimation
Sovatzidi G., Vasilakakis M., Iakovidis D.K. (2022)Depression is a common and serious medical disorder that negatively affects the mood and the emotions of people, especially adolescents. In this paper, a novel framework for automatically creating Fuzzy Cognitive Maps ... -
Energy Use Forecasting with the Use of a Nested Structure Based on Fuzzy Cognitive Maps and Artificial Neural Networks
Poczeta K., Papageorgiou E.I. (2022)The aim of this paper is to present a novel approach to energy use forecasting. We propose a nested fuzzy cognitive map in which each concept at a higher level can be decomposed into another fuzzy cognitive map, multilayer ... -
Soft computing technique of fuzzy cognitive maps to connect yield defining parameters with yield in cotton crop production in central Greece as a basis for a decision support system for precision agriculture application
Papageorgiou, E. I.; Markinos, A. T.; Gemtos, T. A. (2010)This work investigates the yield and yield variability prediction in cotton crop. Cotton crop management is a complex process with interacting parameters like soil, crop and weather factors. The soft computing technique ...