A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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-tekijät tai -toimittajat


Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajatMahini, Reza; Li, Fan; Zarei, Mahdi; Nandi, Asoke K.; Hämäläinen, Timo; Cong, Fengyu

Lehti tai sarjaBiomedical Signal Processing and Control

ISSN1746-8094

eISSN1746-8108

Julkaisuvuosi2023

Ilmestymispäivä02.07.2023

Volyymi86, B

Artikkelinumero105202

KustantajaElsevier

JulkaisumaaBritannia

Julkaisun kielienglanti

DOIhttps://doi.org/10.1016/j.bspc.2023.105202

Julkaisun avoin saatavuusAvoimesti saatavilla

Julkaisukanavan avoin saatavuusOsittain avoin julkaisukanava

Julkaisu on rinnakkaistallennettu (JYX)https://jyx.jyu.fi/handle/123456789/88313


Tiivistelmä

Objective
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.

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.

Results
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.

Conclusion
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.

Significance
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.


YSO-asiasanatklusteritanalyysikognitiiviset prosessittutkimusmenetelmät

Vapaat asiasanatERP


Liittyvät organisaatiot


OKM-raportointiKyllä

Raportointivuosi2023

Alustava JUFO-taso1


Viimeisin päivitys 2024-25-02 klo 18:46