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AKILLI SÄ°STEMLER VE UYGULAMALARI DERGÄ°SÄ°
JOURNAL OF INTELLIGENT SYSTEMS WITH APPLICATIONS
J. Intell. Syst. Appl.
E-ISSN: 2667-6893
Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License.

Design of Steady-State Visually-Evoked Potential Based Brain-Computer Interface System

Durağan-Durum Görsel-Uyarılmış Potansiyel Tabanlı Beyin-Bilgisayar Arayüzü Tasarımı

How to cite: Avcı MB, Hamurcu R, BozbaÅŸ ÃA, Gürman E, Çetin AE, Sayılgan E. Design of steady-state visually-evoked potential based brain-computer interface system. Akıllı Sistemler ve Uygulamaları Dergisi (Journal of Intelligent Systems with Applications) 2022; 5(2): 86-89. DOI: 10.54856/jiswa.202212214

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Title: Design of Steady-State Visually-Evoked Potential Based Brain-Computer Interface System

Abstract: In this study, Steady-State Visual Evoked Potential (SSVEP)-based Brain-Computer Interface (BCI) system, which is popular in many sectors (game, defense, sports, etc.), especially in medicine, was composed. In addition, a robot hand was designed to be integrated into the BCI system, especially to help partially or completely disabled individuals. For this purpose, feature extraction was performed using discrete wavelet transform (Db6) from SSVEP signals recorded from seven different frequencies (6, 6.5, 7, 7.5, 8.2, 9.3, 10 Hz) and four different individuals. Extracted features were classified by support vector machine (SVM) and k-nearest neighbor (k-NN) algorithms. According to the classification results, the highest performance was obtained in the SVM algorithm with an accuracy of 84%.

Keywords: Steady-state visually-evoked potentials; brain-computer interfaces; wavelet transform; machine learning


Başlık: Durağan-Durum Görsel-Uyarılmış Potansiyel Tabanlı Beyin-Bilgisayar Arayüzü Tasarımı

Özet: Bu çalışmada, günümüzde medikal başta olmak üzere bir çok sektörde (oyun, savunma, spor vb.) popüler olan Durağan Durum Görsel Uyarılmış Potansiyel (SSVEP) tabanlı Beyin Bilgisayar Arayüzü (BCI) sistemi oluşturulmuştur. Ayrıca BCI sistemine entegre edilecek, özellikle kısmen veya tamamen engelli bireylere yardımcı olması için robot el tasarımı gerçekleştirilmiştir. Bu amaçla, öncelikle yedi farklı frekanstan (6, 6.5, 7, 7.5, 8.2, 9.3, 10 Hz) ve dört farklı bireyden kaydedilen SSVEP sinyallerinden, ayrık dalgacık dönüşümü (Db6) kullanılarak öznitelik çıkarımı gerçekleştirilmiş. Çıkarılan öznitelikler destek vektör makinesi (SVM) ve k-en yakın komşuluk (k-NN) algoritmaları ile sınıflandırılmıştır. Sınıflandırma sonuçlarına göre en yüksek başarım %84 doğruluk değeri ile SVM algoritmasında elde edilmiştir.

Anahtar kelimeler: Durağan durum görsel uyarılmış potansiyeller; beyin bilgisayar arayüzü; dalgacık dönüşümü; makine öğrenimi


Bibliography:
  • Shih JJ, Krusienski DJ, Wolpaw JR. Brain-computer interfaces in medicine. Mayo Clinic Proceedings 2012; 87(3): 268–279.
  • Binnie CD, Prior PF. Electroencephalography. Journal of Neurology, Neurosurgery, and Psychiatry 1994; 57(11): 1308–1319.
  • Sayilgan E, Yuce YK, Isler Y. Evaluation of mother wavelets on steady-state visually-evoked potentials fortriple-command brain-computer interfaces. Turkish Journal of Electrical Engineering \& Computer Sciences 2021; 29(5): 2263–2279.
  • Sayilgan E, Yuce YK, Isler Y. Frequency recognition from temporal and frequency depth of the brain-computer interface based on steady-state visual evoked potentials. Journal Of Intelligent Systems With Applications 2021; 4(1): 68-73.
  • Degirmenci M, Yuce YK, Isler Y. Evaluation of Wigner-Ville distribution features to estimate steady-state visual evoked potentials' stimulation frequency. Journal of Intelligent Systems with Applications 2021; 4(2): 133-136.
  • Sayilgan E, Karabiber Cura O, Isler Y. Use of clustering algorithms and extreme learning machine in determining arrhythmia types. In 2017 25th Signal Processing and Communications Applications Conference (SIU), May 15-18, 2017, Antalya, Turkey, pp. 1-4.
  • Vilic AV. Avi SSVEP Dataset. 2015, May 26. Retrieved at May 6, 2022, from https://www.setzner.com/avi-ssvep-dataset/
  • Beyrouthy T, Al Kork S, Korbane JA, Abouelela M. EEG mind controlled smart prosthetic arm a comprehensive study. Advances in Science, Technology and Engineering Systems Journal 2017; 2(3): 891-899.
  • Sayilgan E, Yuce YK, Isler Y. Investigating the effect of flickering frequency pair and mother wavelet selection in steady-state visually-evoked potentials on two-command brain-computer interfaces. Innovation and Research in BioMedical Engineering (IRBM) 2022; Corrected Proof, In Press.
  • Sayilgan E, Yuce YK, Isler Y. Determining gaze information from steady-state visually-evoked potentials. Karaelmas Science and Engineering Journal 2020; 10(2): 151-157.
  • Sayilgan E, Yuce YK, Isler Y. Evaluation of wavelet features selected via statistical evidence from steady-state visually-evoked potentials to predict the stimulating frequency. Journal of the Faculty of Engineering and Architecture of Gazi University 2021; 36(2): 593-605.
  • Dunai L, Novak M, Espert CG. Human hand anatomy-based prosthetic hand. Sensors 2020; 21(1): 137.
  • Belter JT, Segil JL, Dollar AM, Weir RF. Mechanical design and performance specifications of anthropomorphic prosthetic hands: A review. Journal of Rehabilitation Research and Development 2013; 50(5): 599-618.
  • Langevin G. InMoov: open-source 3D printed life-size robot. 2012, Retrieved from https://inmoov.fr/
  • Alkhatib F, Mahdi E, Cabibihan JJ, Design and analysis of flexible joints for a robust 3D printed prosthetic hand. In 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR), June 24-28, 2019, Toronto, ON, Canada, pp. 784-789.
  • Wu J, Huang J, Wang Y, Xing K, Xu Q. Fuzzy PID control of a wearable rehabilitation robotic hand driven by pneumatic muscles. In 2009 International Symposium on Micro-NanoMechatronics and Human Science, November 9-11, 2009, Nagoya, Japan, pp. 408-413.