Cognitive Processing and EEG Complexity

Area of Science:

• Cognitive Neuroscience
• Computational Neuroscience
• Neuroscience

Background:

• Cognitive neuroscience traditionally uses electroencephalography (EEG) and event-related potentials (ERPs) to study brain processes.
• Classical ERP analysis often overlooks the inherent non-linear and multiscale dynamics of brain activity.
• There is a growing need to explore EEG complexity for a more comprehensive understanding of cognition.

Purpose of the Study:

• To conduct a narrative review of studies examining non-clinical cognitive processing using EEG complexity measures.
• To focus on EEG metrics derived from fractality, information, and randomness across various scales.
• To integrate findings within a predictability-regularity framework and suggest future research directions.

Main Methods:

• Narrative review of existing literature.
• Focus on complexity measures (fractality, information, randomness) applied to EEG data.
• Analysis across different temporal and spatial scales.

Main Results:

• Complexity measures offer complementary insights beyond traditional linear EEG/ERP analyses.
• Studies reviewed demonstrate the utility of these measures in understanding memory, perception, and attention.
• Findings can be framed within a predictability-regularity continuum of brain dynamics.

Conclusions:

• EEG complexity analysis provides a richer characterization of cognitive brain activity.
• Further research integrating complexity metrics is crucial for advancing our understanding of cognition.
• Future directions include refining complexity measures and their application to diverse cognitive tasks.