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 toimittajat: Zhang, Guanghui; Carrasco, Carlos D.; Winsler, Kurt; Bahle, Brett; Cong, Fengyu; Luck, Steven J.
Lehti tai sarja: Neuroimage
ISSN: 1053-8119
eISSN: 1095-9572
Julkaisuvuosi: 2024
Ilmestymispäivä: 03.05.2024
Volyymi: 293
Artikkelinumero: 120625
Kustantaja: Elsevier
Julkaisumaa: Yhdysvallat (USA)
Julkaisun kieli: englanti
DOI: https://doi.org/10.1016/j.neuroimage.2024.120625
Linkki tutkimusaineistoon: https://doi.org/10.18115/D5JW4R
Julkaisun avoin saatavuus: Avoimesti saatavilla
Julkaisukanavan avoin saatavuus: Kokonaan avoin julkaisukanava
Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/94848
Lisätietoja: Additional 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-asiasanat: EEG; signaalianalyysi; signaalinkäsittely; pääkomponenttianalyysi; koneoppiminen
Vapaat asiasanat: EEG; MVPA; group-based PCA; subject-based PCA; dimensionality reduction
Liittyvät organisaatiot
OKM-raportointi: Kyllä
VIRTA-lähetysvuosi: 2024
Alustava JUFO-taso: 3