Neural Correlates of Mental Workload During Multitasking: a Dynamic Causal Modeling Study

Abstract

The goal of this study is to examine the neural correlates of different mental workload levels. Electroencephalogram (EEG) signals were recorded when participants perform a set of tasks simultaneously with low and high levels of mental workload. Brain connections for each workload level were estimated using Dynamic Causal Modeling (DCM), which is an effective connectivity method to reveal causal relationships between brain sources. The result showed a backward-only, left-lateralized connection pattern for high workload condition, compared to the bidirectional, two-sided connection pattern for low workload condition.These findings of the mental workload effect on neural mechanisms may be utilized in applications of the augmented cognition program.