A1 Journal article (refereed)
Ensemble deep clustering analysis for time window determination of event-related potentials (2023)

Mahini, R., Li, F., Zarei, M., Nandi, A. K., Hämäläinen, T., & Cong, F. (2023). Ensemble deep clustering analysis for time window determination of event-related potentials. Biomedical Signal Processing and Control, 86, B, Article 105202. https://doi.org/10.1016/j.bspc.2023.105202

JYU authors or editors

Publication details

All authors or editorsMahini, Reza; Li, Fan; Zarei, Mahdi; Nandi, Asoke K.; Hämäläinen, Timo; Cong, Fengyu

Journal or seriesBiomedical Signal Processing and Control



Publication year2023

Publication date02/07/2023

Volume86, B

Article number105202


Publication countryUnited Kingdom

Publication languageEnglish


Publication open accessOpenly available

Publication channel open accessPartially open access channel

Publication is parallel published (JYX)https://jyx.jyu.fi/handle/123456789/88313


Cluster analysis of spatio-temporal event-related potential (ERP) data is a promising tool for exploring the measurement time window of ERPs. However, even after preprocessing, the remaining noise can result in uncertain cluster maps followed by unreliable time windows while clustering via conventional clustering methods.

We designed an ensemble deep clustering pipeline to determine a reliable time window for the ERP of interest from temporal concatenated grand average ERP data. The proposed pipeline includes semi-supervised deep clustering methods initialized by consensus clustering and unsupervised deep clustering methods with end-to-end architectures. Ensemble clustering from those deep clusterings was used by the designed adaptive time window determination to estimate the time window.

After applying simulated and real ERP data, our method successfully obtained the time window for identifying the P3 components (as the interest of both ERP studies) while additional noise (e.g., adding 20 dB to −5 dB white Gaussian noise) was added to the prepared data.

Compared to the state-of-the-art clustering methods, a superior clustering performance was yielded from both ERP data. Furthermore, more stable and precise time windows were elicited as the noise increased.

Our study provides a complementary understanding of identifying the cognitive process using deep clustering analysis to the existing studies. Our finding suggests that deep clustering can be used to identify the ERP of interest when the data is imperfect after preprocessing.

Keywordsclustersanalysiscognitive processesresearch methods

Free keywordsevent-related potentials; time window; deep clustering; ensemble learning; consensus clustering; ERP microstates

Contributing organizations

Ministry reportingYes

Reporting Year2023

Preliminary JUFO rating1

Last updated on 2024-18-02 at 18:26