Reliability Analysis of EEG Microstate Features in Patients With Major Depressive Disorder at Different Electrode Configurations
Electroencephalogram (EEG) microstate analysis is a promising spatiotemporal approach widely used to investigate brain activity in psychiatric disorders. However, the reliability of the results can vary depending on the choice of data configuration. This study aimed to investigate the influence of electrode numbers on the reliability of microstates and to recommend a minimum electrode number for microstate analysis in psychiatric disorders. Microstate topologies and parameters were compared across different electrode configurations (105-, 59-, 27-, 19-, and 9-channel) in health controls (HC) and patients with major depressive disorder (MDD), respectively. Similarity (S) and Cronbach α were employed to quantify the reliability of microstate topologies and parameters for both HC and MDD groups. Statistical comparisons between the HC and MDD groups were conducted to assess the stability of microstate differences across different electrode configurations, with Cronbach α calculated for reliability. The results revealed minimally significant differences in microstate features between the 105- and 59-channel configurations in both HC and MDD groups. Cronbach α also indicated that the agreement between the 105- and 59-channel configurations was superior to that of the other electrode configurations. Furthermore, the significance of the differences between the HC and MDD groups remained stable with the 105- and 59-channel configurations. Cronbach α demonstrated the highest agreement between the 105-channel and 59-channel configurations. This study concludes that resting EEG microstate analysis in MDD patients is highly reliable with a minimum of 59 electrodes.
Authors
- Shuo Yang (ORCID: https://orcid.org/0000-0002-4986-4761)
- Lei Wang (ORCID: https://orcid.org/0000-0002-8625-3652)
- Chen Wang (ORCID: https://orcid.org/0009-0009-7541-7760)
- Shen Yang (ORCID: https://orcid.org/0009-0007-7283-4994)
Institutions
- Hebei University of Technology (CN)
Publication Details
- Journal
- IEEE Access
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1109/access.2025.3560797
- Primary Topic
- Neuroscience and Neural Engineering
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China