DHGUP TABANLI BEYİN-BİLGİSAYAR ARAYÜZLERİ İÇİN ALT UZAY KEŞFİ

Abdullah Kutay Cankı; Hüseyin Özkan

doi:10.17780/ksujes.1175402

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

DHGUP TABANLI BEYİN-BİLGİSAYAR ARAYÜZLERİ İÇİN ALT UZAY KEŞFİ

Öz

Bu makalede, günümüz modern uygulamalarında hızla artan veri miktar ve boyutlarının sebep olduğu algoritma eğitme sorunlarının çözümüne yönelik, kompakt veri özniteliklerinin öğrenilmesi amacıyla bir boyut indirgeme metodu önerilmiştir. Verinin boyutu yüksek olsa da, genellikle taşıdığı bilgi daha düşük boyutlu alt uzaylarda yaşar. Çalışmamızda, böyle alt uzayların bir kümesel birleşimi burada geliştirdiğimiz yenilikçi bir ileri beslemeli sinir ağı ile algoritmik öğrenilmiştir. İlaveten, bu bağlamdaki sınıflandırma problemleri üzerinde durduk. Metodumuzun performansı öncelikle kendi oluşturduğumuz bir yapay veri seti üzerinde incelenmiştir. Sonrasında ise, durağan hal görsel uyarılmış potansiyel (DHGUP) tabanlı beyin-bilgisayar arayüzü (BBA) heceletici sistemleri için sıkça kullanılan genel kullanıma açık bir veri seti üzerinde metodumuz test edilmiştir. Sonuçlar, metodumuzun alt uzayları başarıyla bulabildiğini ve diğer DHGUP BBA heceletici hedef karakter tanıma metotlarından makul zaman aralıklarında daha iyi bir performans (0,8 saniye sinyal uzunluğunda 156 bit/dk’lık veri aktarım hızı) verdiğini göstermiştir.

Anahtar Kelimeler

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.

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Details

Primary Language

Turkish

Subjects

Computer Software , Electrical Engineering

Journal Section

Research Article

Authors

Abdullah Kutay Cankı
0000-0002-4485-6826
Türkiye

Hüseyin Özkan ^*
0000-0002-5539-9085
Türkiye

Publication Date

March 15, 2023

Submission Date

September 14, 2022

Acceptance Date

November 19, 2022

Published in Issue

Year 2023 Volume: 26 Number: 1

DOI

https://doi.org/10.17780/ksujes.1175402

IZ

https://izlik.org/JA98YJ77YG

Cite

RIS / Bibtex

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