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 toimittajat: Mahini, Reza; Li, Fan; Zarei, Mahdi; Nandi, Asoke K.; Hämäläinen, Timo; Cong, Fengyu
Lehti tai sarja: Biomedical Signal Processing and Control
ISSN: 1746-8094
eISSN: 1746-8108
Julkaisuvuosi: 2023
Ilmestymispäivä: 02.07.2023
Volyymi: 86, B
Artikkelinumero: 105202
Kustantaja: Elsevier
Julkaisumaa: Britannia
Julkaisun kieli: englanti
DOI: https://doi.org/10.1016/j.bspc.2023.105202
Julkaisun avoin saatavuus: Avoimesti saatavilla
Julkaisukanavan avoin saatavuus: Osittain avoin julkaisukanava
Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/88313
Tiivistelmä
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-asiasanat: klusterit; analyysi; kognitiiviset prosessit; tutkimusmenetelmät
Vapaat asiasanat: ERP
Liittyvät organisaatiot
OKM-raportointi: Kyllä
VIRTA-lähetysvuosi: 2023
JUFO-taso: 1
