Research Article

SUBSPACE DISCOVERY FOR SSVEP BASED BRAIN-COMPUTER INTERFACES

Volume: 26 Number: 1 March 15, 2023
EN TR

SUBSPACE DISCOVERY FOR SSVEP BASED BRAIN-COMPUTER INTERFACES

Abstract

In this paper, a dimensionality reduction method is proposed for the learning of compact data features in order to mitigate the algorithm training issues caused by the increasing data size and dimensionalities in today’s modern applications. Although the dimension of data is high, most of the information typically lives in lower dimensional subspaces. In our study, a set combination of such subspaces is algorithmically learned by a novel feed forward neural network that we develop here. Additionally, we consider classification problems in this context. The performance of our method is first evaluated on a synthetic dataset that we generated. Afterwards, our method is tested on a publicly open and widely used steady state visually evoked potentials (SSVEP)-based brain-computer interface (BCI) speller system dataset. The results reveal that our method successfully finds the subspaces and delivers a superior performance (156 bit/min information transfer rate at 0,8 seconds of signal length) than other SSVEP BCI speller target character recognition methods in reasonable time intervals.

Keywords

Supporting Institution

Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK)

Project Number

118E268

Thanks

Bu çalışma, Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK) tarafından 118E268 numaralı proje kapsamında desteklenmiş, ve birinci yazar Abdullah Kutay Cankı’nın yüksek lisans tezi kapsamında yapılmıştır. Yazım ve deney aşamalarındaki yardımlarından dolayı Osman Berke Güney’e teşekkür ederiz.

References

  1. Abdi, H. & Williams, L.J. (2010). Principal component analysis. Wiley Interdisciplinary Reviews: Computational Statistics, 2(4), 433-459. https://doi.org/10.1002/wics.101
  2. Dietterich, T. G. & Bakiri, G. (1994). Solving multiclass learning problems via error-correcting output codes. Journal of Artificial Intelligence Research, 2(1), 263-286. https://dl.acm.org/doi/10.5555/1622826.1622834
  3. Ghodsi, A. (2006). Dimensionality reduction a short tutorial. Department of Statistics and Actuarial Science at Univ. of Waterloo, Ontario, Canada, 37(38). https://www.math.uwaterloo.ca/~aghodsib/courses/f06stat890/readings/tutorial_stat890.pdf
  4. Guney, O. B., Oblokulov, M. & Ozkan, H. (2022). A deep neural network for ssvep-based brain-computer interfaces. IEEE Transactions on Biomedical Engineering, 69(2), 932-944. https://doi.org/10.1109/TBME.2021.3110440
  5. Kim, C. & Klabjan, D. (2019). A simple and fast algorithm for L1-norm kernel PCA. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(8), pp.1842-1855. https://doi.org/10.1109/TPAMI.2019.2903505
  6. Kingma, D.P. & Welling, M. (2019). An introduction to variational autoencoders. Foundations and Trends in Machine Learning, 12(4), 307-392. http://dx.doi.org/10.1561/2200000056
  7. Kolda, T. G. & Bader, B. W. (2009). Tensor decompositions and applications. SIAM review, 51(3), 455-500. https://doi.org/10.1137/07070111X
  8. Krauledat, M., Tangermann, M., Blankertz, B. & Müller, K. R. (2008). Towards zero training for brain-computer interfacing. PloS one, 3(8), e2967. https://doi.org/10.1371/journal.pone.0002967

Details

Primary Language

Turkish

Subjects

Computer Software , Electrical Engineering

Journal Section

Research Article

Authors

Abdullah Kutay Cankı
0000-0002-4485-6826
Türkiye

Publication Date

March 15, 2023

Submission Date

September 14, 2022

Acceptance Date

November 19, 2022

Published in Issue

Year 1970 Volume: 26 Number: 1

APA
Cankı, A. K., & Özkan, H. (2023). DHGUP TABANLI BEYİN-BİLGİSAYAR ARAYÜZLERİ İÇİN ALT UZAY KEŞFİ. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 26(1), 86-97. https://doi.org/10.17780/ksujes.1175402