Capturing the Forest but Missing the Trees: Microstates Inadequate for Characterizing Shorter-Scale EEG Dynamics

Area of Science:

• Neuroscience
• Cognitive Science
• Dynamical Systems Theory

Background:

• Resting-state brain activity is often studied using fMRI networks.
• EEG microstate analysis, based on global field potential (GFP) peaks, offers an alternative method.
• Microstates are hypothesized as discrete 'atoms of thought' for cognitive processing.

Discussion:

• This study critically examines the assumptions of EEG microstate analysis.
• Dynamical systems, fractal, and chaos theory are employed to analyze temporal dynamics and clustering.
• Shortcomings in microstate analysis are highlighted, particularly concerning data outside GFP peaks.

Key Insights:

• Complex and chaotic EEG dynamics outside GFP peaks are currently missed by microstate analysis.
• The 'winner-takes-all' assumption of single microstate activity is inadequate; competition exists.
• The four canonical microstates do not form distinct, separable clusters in the analysis space.

Outlook:

• The discontinuous model of EEG microstates is insufficient for capturing non-stationary, short-scale EEG dynamics.
• Future research should explore more continuous and comprehensive models of brain activity.
• Re-evaluating the interpretation of EEG microstates in cognitive neuroscience is necessary.