Analysis of EEG microstate transition patterns and multi-instance learning-based recognition model for depression.

Accurate identification of major depressive disorder (MDD) and characterization of abnormal higher-order brain-state transitions remain important in electroencephalography (EEG)-based depression research. Although EEG microstate analysis provides a promising high-temporal-resolution tool for probing abnormal brain dynamics, most existing studies focus on static microstate parameters and may overlook higher-order transition structure embedded in microstate sequences. To address this issue, we proposed a rule-driven microstate sequence analysis framework for EEG-based depression recognition. Specifically, the RuleGrowth algorithm was used to mine discriminative microstate transition rules, which were incorporated into an attention-based multiple instance learning model for classification. The proposed method outperformed conventional approaches based on static microstate features on both datasets, achieving accuracy, F1-score, and area under the curve (AUC) values of 96.67%, 94.12%, and 97.23% on the multi-modal open dataset for mental disorder analysis (MODMA) dataset, and 92.67%, 92.78%, and 94.50% on the self-collected dataset. In addition, both datasets showed significant group differences in higher-order transition patterns after false discovery rate (FDR) correction ([Formula: see text]), with 7 and 13 significant rules identified, respectively, and the C→D→B and A→D→B rules consistently showing higher confidence in the MDD group than in healthy controls across both datasets. These findings indicate that MDD is associated with disrupted higher-order brain-state switching dynamics and support the utility of rule-based microstate representations for EEG-based depression analysis.