Effective connectivity of mental fatigue: Dynamic casual modeling of EEG data

BACKGROUND: Recognition of sources in the brain and their interaction with mental fatigue states are interesting subjects for researchers. OBJECTIVE: The aim of this study was to investigate the mental fatigue effects on brain areas by dynamic casual modeling (DCM) parameters that are extracted from event-related potential (ERP) signals which were then estimated based on mental fatigue data with visual stimulation. METHODS: ERP were recorded based on a Continuous Performance Task in four consecutive trials. Active regions and brain sources were extracted by a Multiple Sparse Priors algorithm. RESULTS: Four models are proposed for DCM. The parameters and the structure of the best model were obtained by SPM software for ERP in each of the four trials. CONCLUSION: The results illustrate that an increase of mental fatigue through trials leads to increased likelihood of choosing forward models.