EEG microstate transition cost correlates with task demands

Area of Science:

• Neuroscience
• Cognitive Science
• Computational Neuroscience

Background:

• Complex task performance depends on adaptable brain activity dynamics.
• Impaired cognitive flexibility can lead to attention deficits and reduced efficiency.
• Quantifying cognitive effort using neural data is crucial for understanding performance limitations.

Purpose of the Study:

• To propose a framework for associating cognitive effort with electroencephalography (EEG) activation patterns.
• To develop a data-driven method for measuring cognitive effort during task performance.
• To validate the framework using a spatial Stroop task.

Main Methods:

• Utilized electroencephalography (EEG) to record brain activity.
• Identified discrete dynamical states known as EEG microstates.
• Applied optimal transport theory to quantify transitions between states.

Main Results:

• The proposed framework successfully quantified cognitive effort during task performance.
• An increased 'cost' associated with cognitive effort was identified.
• The method demonstrated effectiveness in capturing and quantifying cognitive transitions.

Conclusions:

• The developed framework provides a quantitative measure of cognitive effort.
• This approach offers new insights into the physiological basis of cognitive effort.
• The data-driven methodology enhances understanding of brain dynamics during complex tasks.