Negin MANSHOURI, Mesut MELEK, Temel KAYIKCIOGLU

The Efficacy of Frontal and Temporal Lobes in Detecting 2D&3D Video Transition Using EEG

Due to the three-dimensional (3D) structure of the human eye, 3D technology was used in this research. Transition to 2D and 3D analysis is important, claiming that binocular vision will lose dimension during fatigue. Thus, a single-stream video consisting of random 2D&3D parts was watched by nine volunteers. The dynamic behavior and power spectral density (PSD) of the volunteers’ brain signals were considered for a comprehensive quantitative analysis. The dominant EEG bands and time intervals were selected in 2D to 3D (2D_3D) and 3D to 2D (3D_2D) transitions by analyzing power differences based on short-time Fourier transformation (STFT). Taking into account this information, applying effective feature extraction and classification techniques, the behavioral patterns of channels representing the brain lobes of the volunteers were analyzed. Hjorth parameters and statistical methods were used as feature extraction methods. The k-nearest neighbors (k-NN) and linear discriminant analysis (LDA) algorithms were applied to classify 2D_3D and 3D_2D transitions. The results revealed that, thanks to the comprehensive classification analysis of the 2D_3D and 3D_2D transitions, the change in the activity power of the brain cortex can be represented. The dominance of the temporal and frontal lobes between the channels representing these transitions cannot be excluded.

EEG Kullanarak 2D&3D Video Geçişinin Tespitinde Frontal ve Temporal Lobların Etkinliği

İnsan gözünün üç boyutlu (3B) yapısı nedeniyle, bu araştırmada 3B teknolojisi kullanılmıştır. Binoküler görmenin yorgunluk esnasında boyut kaybedeceğini iddia ederek 2B ve 3B’ye geçiş analizi önemlidir. Böylece rastgele 2B ve 3B parçalardan oluşan tek akışlı bir video dokuz gönüllü tarafından izlenilmiştir. Gönüllülerin beyin sinyallerinin dinamik davranışı ve güç spektral yoğunluğu (GSY), kapsamlı bir nicel analiz için dikkate alınmıştır. Baskın EEG bantları ve zaman aralıkları, kısa zamanlı Fourier dönüşümüne (KZFD) dayalı güç farklılıklarını analiz ederek 2B’den 3B’ye (2B_3B) ve 3B’den 2B’ye (3B_2B) geçişlerde seçilmiştir. Bu bilgiler dikkate alınarak, etkili özellik çıkarımı ve sınıflandırma teknikleri uygulanarak, gönüllülerin beyin loblarını temsil eden kanalların davranış kalıpları analiz edilmiştir. Öznitelik çıkarma yöntemleri olarak Hjorth parametreleri ve istatistiksel yöntemler kullanılmıştır. 2B_3B ve 3B_2B geçişlerini sınıflandırmak için en yakın komşular (e-YK) ve doğrusal diskriminant analiz (DDA) algoritmaları uygulanmıştır. Sonuçlar, 2B_3B ve 3B_2B geçişlerinin kapsamlı sınıflandırma analizi sayesinde beyin korteksinin aktivite gücünün değişiminin temsil edilebileceğini ortaya koymuştur. Bu geçişleri temsil eden kanallar arasındaki temporal ve frontal lobların baskınlığı göz ardı edilemez.

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