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Dergiler / Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi / 2025 / Cilt: 25 Sayı: 6

EOG Artefaktları Kullanılarak Yörünge Tabanlı Sınıflandırma: Felçli Bireyler İçin Alternatif Bir İnsan-Makine Arayüz Yaklaşımı

Comparison of ADD and GSY Methods with Machine Learning Algorithms Using EOG Artifacts in EEG Data

Sefa Aydın(GÜMÜŞHANE ÜNİVERSİTESİ, GÜMÜŞHANE MESLEK YÜKSEKOKULU)0000-0001-9965-2903
Mesut Melek(GÜMÜŞHANE ÜNİVERSİTESİ, GÜMÜŞHANE MÜHENDİSLİK FAKÜLTESİ)0000-0002-7152-7788
Levent Gökrem(TOKAT GAZİOSMANPAŞA ÜNİVERSİTESİ)0000-0003-2101-5378
Sayfa
1491–1510
Yayın Tarihi
2025-12-03
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Karşılaştırılan iki yöntemden GSY’nin ADD’ye göre daha yüksek doğruluk oranları ve ITR değerleri verdiği, LDA algoritmasının k-EYK algoritmasından daha başarılı olduğu gözlemlenmiştir.

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Amaç & Hipotez:Günümüzde, doğuştan veya omurilik yaralanması gibi sinir sistemi problemleri sebebiyle tamamen felç olmuş bireylerin günlük ihtiyaçlarını karşılayabilmeleri için Beyin-bilgisayar arayüz (BBA) sistemleri geliştirilmiştir.
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Temel Bulgular & Veri:Fakat BBA sistemlerinin kullanıcılar üzerinde bazı olumsuz etkileri vardır.
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Sonuç & Katkı:Karşılaştırılan iki yöntemden GSY’nin ADD’ye göre daha yüksek doğruluk oranları ve ITR değerleri verdiği, LDA algoritmasının k-EYK algoritmasından daha başarılı olduğu gözlemlenmiştir.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:8 denek
⚙️Değişkenler:Beyin-bilgisayar Arayüz · Elektrosefalografi · Elektrookülografi

Özet

Günümüzde, doğuştan veya omurilik yaralanması gibi sinir sistemi problemleri sebebiyle tamamen felç olmuş bireylerin günlük ihtiyaçlarını karşılayabilmeleri için Beyin-bilgisayar arayüz (BBA) sistemleri geliştirilmiştir. BBAlar, çeşitli cihazları kullanarak topladığı beyin sinyallerini bilgisayarlar aracılığıyla işleyerek, çevre cihazları harekete geçirmek için çıktı komutları üreten sistemlerdir. Fakat BBA sistemlerinin kullanıcılar üzerinde bazı olumsuz etkileri vardır. Bu çalışmada, Görsel uyaran potansiyel (GUP) ve P300 gibi görsel uyaran tabanlı BBA sistemlerinde kullanılan farklı frekans değerlerinde titreşen uyaranların sistem kullanıcılarının göz sağlığı üzerinde meydana getirdiği olumsuz etkiyi azaltmak amacıyla; hareketli nesneler yaklaşımını kullanan bir İnsan-Makine Arayüz (İMA) sistemi önerilmiştir. Sistem, kullanıcının hareketli topları odaklanarak takip etmesi sonucunda Elektrosefalografi (EEG) sinyallerinde meydana gelen Elektrookülografi (EOG) artefaktlarını kullanarak hareket yörüngelerini makine öğrenme algoritmaları ile sınıflandırmayı amaçlamaktadır. Çalışma, Emotiv EPOC EEG cihazı aracılığıyla dört farklı rotada hareket eden toplar içeren yaklaşım kullanılarak sağlıklı 8 denek üzerinde gerçekleştirilmiştir. Kaydedilen ham verilere ön işleme aşamaları uygulanmıştır ve etkin kanal seçimi yapılmıştır. Tespit edilen etkin kanallara (AF3, AF4) ait verilere Güç Spektral Yoğunluğu (GSY) ve Ayrık Dalgacık Dönüşümü (ADD) yöntemleri uygulanmıştır. Her yöntem için öznitelikler çıkartılmış ve k-en yakın komşu (k-EYK) ve Lineer Diskriminant Analiz (LDA) algoritmaları kullanılarak sınıflandırılmıştır. GSY yöntemi için LDA ve k-EYK algoritmaları ile sırasıyla 93.86%, 92.62% doğruluk oranları ve 31.42 (bit/dk), 30.10 (bit/dk) ITR değerleri hesaplanmıştır. ADD yöntemi için ise sırasıyla 92.84%, 92.17% doğruluk oranları ve 30.36 (bit/dk), 29.62 (bit/dk) ITR değerleri elde edilmiştir. Karşılaştırılan iki yöntemden GSY’nin ADD’ye göre daha yüksek doğruluk oranları ve ITR değerleri verdiği, LDA algoritmasının k-EYK algoritmasından daha başarılı olduğu gözlemlenmiştir.

Abstract

Today, Brain-computer interface (BCI) systems have been developed to meet the daily needs of individuals who are completely paralyzed due to nervous system problems such as congenital or spinal cord injury. BCIs are systems that produce output commands to activate peripheral devices by processing the brain signals collected using various devices through computers. However, BCI systems also have some negative effects on system users. In this study, a Human-Machine Interface (HMI) system that uses the moving objects approach is proposed in order to reduce the negative effects of visual stimuli vibrating at different frequency values used in Visual Evoked Potential (VEP) and P300-based BCI systems on the eye health of system users. The system aims to classify movement trajectories with machine learning algorithms by using Electrooculography (EOG) artifacts that occur in Electrocephalography (EEG) signals as a result of the user focusing on moving balls. The study was conducted on 8 healthy subjects using an approach involving balls moving in four different routes through the Emotiv EPOC EEG device. Pre-processing stages were applied to the recorded raw data and effective channel selection was made. Power Spectral Density (PSD) and Discrete Wavelet Transform (DWT) methods were applied to the data of the detected active channels (AF3, AF4). Features were extracted for each method and classified using k-Nearest Neighbor (k-NN) and Linear Discriminant Analysis (LDA) algorithms. For the PSD method, 93.86%, 92.62% accuracy rates and 31.42 (bit/min), 30.10 (bit/min) ITR values were calculated with the LDA and k-NN algorithms, respectively. For the DWT method, accuracy rates of 92.84%, 92.17% and ITR values of 30.36 (bit/min), 29.62 (bit/min) were obtained, respectively. It has been observed that, among the two compared methods, PSD gives higher accuracy rates and ITR values than DWT, and the LDA algorithm is more successful than the k-NN algorithm.

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Bu Makaleye Atıf Yap

Aydın, S., Melek, M., Gökrem, L. (2025). EOG Artefaktları Kullanılarak Yörünge Tabanlı Sınıflandırma: Felçli Bireyler İçin Alternatif Bir İnsan-Makine Arayüz Yaklaşımı. Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, Cilt: 25 Sayı: 6, 1491–1510. https://doi.org/10.35414/akufemubid.1643728

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