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Principal component analysis and manifold learning techniques for the design of brain-computer interfaces based on steady-state visually evoked potentialsYesilkaya, Bartu ...Steady-state visually evoked potentials (SSVEP) are stochastic and nonstationary bioelectric signals. Because of these properties, it is difficult to achieve high classification accuracy, especially ... when many considered features lead to a complex structure. We therefore propose a manifold learning framework to decrease the number of features and to classify SSVEP data by comparing lower dimensional matrices with well-known machine learning algorithms. We use the AVI-SSVEP Dataset, which includes stimuli at seven different frequencies and 15360 samples per person. The SSVEP features are extracted from relevant and distinctive frequency-domain, time-domain, and time–frequency domain properties, creating a total of 55 feature vectors. We then analyze and compare five divergent manifold learning methods with respect to their performance on nine different machine-learning algorithms. Among all considered manifold learning methods, we show that the Principal Component Analysis has the best classifier performance with an average of 22 components. Moreover, the Naive Bayes classifier with the Principal Component Analysis achieves the maximum accuracy of 50.0%–80.95% for a 7-class classification problem. Our research thus shows that the proposed analytical framework can significantly improve the decoding accuracy of 7-class SSVEP problems, and that it exhibits notable robustness and efficiency for small group datasets.Vir: Journal of computational science. - ISSN 1877-7503 (Vol. 68, [article no.] 102000, Apr. 2023, str. 1-9)Vrsta gradiva - članek, sestavni delLeto - 2023Jezik - angleškiCOBISS.SI-ID - 146916099
Avtor
Yesilkaya, Bartu |
Sayilgan, Ebru |
Yuce, Yilmaz Kemal |
Perc, Matjaž |
Isler, Yalcin
Teme
raznoliko učenje |
vmesnik možgani-računalnik |
ravnovesni vizualno vzpodbujeni potencijal |
analiza glavnih komponent |
zmanjšanje funkcij |
ne zaključna dela |
manifold learning |
brain-computer interface |
steady-state visual evoked potential |
principal component analysis |
feature reduction
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Avtor | Yesilkaya, Bartu ... |
Naslov | Principal component analysis and manifold learning techniques for the design of brain-computer interfaces based on steady-state visually evoked potentials |
Datum objave | |
COBISS.SI-ID | 146916099 |
Verzija objave v repozitoriju | Ni določeno |
Licenca objave v repozitoriju | Ni določeno |
Embargo | Ni določeno |
Projekti, iz katerih je bila financirana objava
Naziv | Akronim | Številka projekta | Financer |
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Računsko intenzivni kompleksni sistemi | P1-0403-2019 |
Javna agencija za znanstvenoraziskovalno in inovacijsko dejavnost Republike Slovenije |
|
Fazni prehodi proti koordinaciji v večplastnih omrežjih | J1-2457-2020 |
Javna agencija za znanstvenoraziskovalno in inovacijsko dejavnost Republike Slovenije |
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Yesilkaya, Bartu | ![]() |
Sayilgan, Ebru | ![]() |
Yuce, Yilmaz Kemal | ![]() |
Perc, Matjaž | 23428 |
Isler, Yalcin | ![]() |
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