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AKILLI SİSTEMLER VE UYGULAMALARI DERGİSİ
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, Bozbaş A, 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

Full Text: PDF, in English.

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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


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