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JOURNAL OF INTELLIGENT SYSTEMS WITH APPLICATIONS
J. Intell. Syst. Appl.
E-ISSN: 2667-6893

Frequency Recognition from Temporal and Frequency Depth of the Brain-Computer Interface based on Steady-State Visual Evoked Potentials

Durağan-Durum Görsel Uyarılmış Potansiyellere Dayalı Beyin-Bilgisayar Arayüzünün Zamansal ve Frekanssal Derinliğinden Frekans Tanıma

How to cite: Sayılgan E, Yüce YK, İşler Y. Frequency recognition from temporal and frequency depth of the brain-computer interface based on steady-state visual evoked potentials. J Intell Syst Appl 2021; 4(1): 68-73.

Full Text: PDF, in English.

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Title: Frequency Recognition from Temporal and Frequency Depth of the Brain-Computer Interface based on Steady-State Visual Evoked Potentials

Abstract: Brain-computer interface (BCI) system based on steady-state visual evoked potentials (SSVEP) have been acceleratingly used in different application areas from entertainment to rehabilitation, like clinical neuroscience, cognitive, and use of engineering researches. Of various electroencephalography paradigms, SSVEP-based BCI systems enable apoplectic people to communicate with outside world easily, due to their simple system structure, short or no training time, high temporal resolution, high information transfer rate, and affordable by comparing to other methods. SSVEP-based BCIs use multiple visual stimuli flickering at different frequencies to generate distinct commands. In this paper, we compared the classifier performances of combinations of binary commands flickering at seven different frequencies to determine which frequency pair gives the highest performance using temporal and spectral methods. For SSVEP frequency recognition, in total 25 temporal change characteristics of the signals and 15 frequency-based feature vectors extracted from the SSVEP signal. These feature vectors were applied to the input of seven well-known machine learning algorithms (Decision Tree, Discriminant Analysis, Logistic Regression, Naive Bayes, Support Vector Machines, Nearest Neighbour, and Ensemble Learning). In conclusion, we achieved 100% accuracy in 7.5 - 10 frequency pairs among these 2,520 distinct runs and we found that the most successful classifier is the Ensemble Learning classifier. The combination of these methods leads to an appropriate detailed and comparative analysis that represents the robustness and effectiveness of classical approaches.

Keywords: Brain-computer interface; steady-state visual-evoked potential; EEG; machine learning


Başlık: Durağan-Durum Görsel Uyarılmış Potansiyellere Dayalı Beyin-Bilgisayar Arayüzünün Zamansal ve Frekanssal Derinliğinden Frekans Tanıma

Özet: Durağan-durum görsel-uyarılmış potansiyellere (DDGUP) dayalı beyin-bilgisayar arayüzü (BBA) sistemi, klinik nörobilim, bilişsel ve mühendislik araştırmalarının kullanımı gibi eğlenceden rehabilitasyona kadar farklı uygulama alanlarında hızla kullanılmaktadır. Çeşitli elektroensefalografi paradigmalarından DDGUP tabanlı BBA sistemleri, apoplektik kişilerin basit sistem yapıları, kısa veya hiç eğitim süreleri, yüksek zamansal çözünürlükleri, yüksek bilgi aktarım hızları ve diğer yöntemlere göre ekonomik olması nedeniyle dış dünya ile kolayca iletişim kurmasını sağlar. DDGUP tabanlı BBA'lar, farklı komutlar oluşturmak için farklı frekanslarda titreşen birden çok görsel uyarıcı kullanır. Bu yazıda, zamansal ve spektral yöntemleri kullanarak hangi frekans çiftinin en yüksek performansı verdiğini belirlemek için yedi farklı frekansta titreşen ikili komut kombinasyonlarının sınıflandırıcı performanslarını karşılaştırdık. DDGUP’tan frekans tanıma için, DDGUP sinyalinden toplam 25 zamansal değişim özniteliği ve 15 frekans tabanlı öznitelik vektörü çıkarıldı. Bu öznitelik vektörleri, iyi bilinen yedi makine öğrenme algoritmasının (Karar Ağacı, Ayırıcı Analiz, Lojistik Regresyon, Naive Bayes, Destek Vektör Makineleri, En Yakın Komşuluk ve Topluluk Öğrenmesi) girdisine uygulandı. Sonuç olarak, 2,520 farklı koşturma arasında 7.5 - 10 frekans çiftinde %100 doğruluk elde ettik ve en başarılı sınıflandırıcının Topluluk Öğrenmesi sınıflandırıcısı olduğunu gördük. Bu yöntemlerin kombinasyonu, klasik yaklaşımların sağlamlığını ve etkinliğini temsil eden uygun, ayrıntılı ve karşılaştırmalı bir analize götürmektedir.

Anahtar kelimeler: Beyin-bilgisayar arayüzü; durağan durum görsel uyarılmış potansiyel; EEG; makine öğrenmesi


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