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This study explores the analysis of EEG signals to understand brain microstates and their dynamics in schizophrenia. Key aspects include:

  1. Transition Matrix and Graphs: Creation of transition matrices and directed graphs to quantify and visualize EEG-derived microstate transitions in individuals with schizophrenia and healthy controls.

  2. Motif Analysis: Identification of recurring microstate transition patterns (motifs) within the complex networks, offering insights into neural circuitry differences in schizophrenia.

  3. Comparative Analysis: Utilization of statistical methods to compare motif distributions between groups, highlighting motifs that significantly differ in schizophrenia.

  4. Implications: The study discusses how certain EEG microstate motifs could serve as potential biomarkers for schizophrenia, aiding in diagnostics and therapeutic strategies.

This research integrates statistical techniques and graph theory.