A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Assessing the effectiveness of spatial PCA on SVM-based decoding of EEG data (2024)


Zhang, G., Carrasco, C. D., Winsler, K., Bahle, B., Cong, F., & Luck, S. J. (2024). Assessing the effectiveness of spatial PCA on SVM-based decoding of EEG data. Neuroimage, 293, Article 120625. https://doi.org/10.1016/j.neuroimage.2024.120625


JYU-tekijät tai -toimittajat


Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajatZhang, Guanghui; Carrasco, Carlos D.; Winsler, Kurt; Bahle, Brett; Cong, Fengyu; Luck, Steven J.

Lehti tai sarjaNeuroimage

ISSN1053-8119

eISSN1095-9572

Julkaisuvuosi2024

Ilmestymispäivä03.05.2024

Volyymi293

Artikkelinumero120625

KustantajaElsevier

JulkaisumaaYhdysvallat (USA)

Julkaisun kielienglanti

DOIhttps://doi.org/10.1016/j.neuroimage.2024.120625

Linkki tutkimusaineistoonhttps://doi.org/10.18115/D5JW4R

Julkaisun avoin saatavuusAvoimesti saatavilla

Julkaisukanavan avoin saatavuusKokonaan avoin julkaisukanava

Julkaisu on rinnakkaistallennettu (JYX)https://jyx.jyu.fi/handle/123456789/94848

LisätietojaAdditional data and scripts are available at https://osf.io/tgzew/


Tiivistelmä

Principal component analysis (PCA) has been widely employed for dimensionality reduction prior to multivariate pattern classification (decoding) in EEG research. The goal of the present study was to provide an evaluation of the effectiveness of PCA on decoding accuracy (using support vector machines) across a broad range of experimental paradigms. We evaluated several different PCA variations, including group-based and subject-based component decomposition and the application of Varimax rotation or no rotation. We also varied the numbers of PCs that were retained for the decoding analysis. We evaluated the resulting decoding accuracy for seven common event-related potential components (N170, mismatch negativity, N2pc, P3b, N400, lateralized readiness potential, and error-related negativity). We also examined more challenging decoding tasks, including decoding of face identity, facial expression, stimulus location, and stimulus orientation. The datasets also varied in the number and density of electrode sites. Our findings indicated that none of the PCA approaches consistently improved decoding performance related to no PCA, and the application of PCA frequently reduced decoding performance. Researchers should therefore be cautious about using PCA prior to decoding EEG data from similar experimental paradigms, populations, and recording setups.


YSO-asiasanatEEGsignaalianalyysisignaalinkäsittelypääkomponenttianalyysikoneoppiminen

Vapaat asiasanatEEG; MVPA; group-based PCA; subject-based PCA; dimensionality reduction


Liittyvät organisaatiot


OKM-raportointiKyllä

VIRTA-lähetysvuosi2024

Alustava JUFO-taso3


Viimeisin päivitys 2024-24-07 klo 10:31