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Journals / Dicle Tıp Dergisi / 2019 / Cilt: 46 - Sayı: 1

Classification of Magnetoencephalography Signals Regarding Visual Stimuli by Generalized Regression Neural Network

Görsel Uyaranlara İlişkin Manyetoensefalografi Sinyallerinin Genelleştirilmiş Regresyon Sinir Ağı ile Sınıflandırılması

ONURSAL ÇETİN
FEYZULLAH TEMURTAŞ
Pages
18–25
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Conclusion: GRNN is thought to be a successful alternative to existing methods for classifying MEG signals.

📌 3 Key Takeaways

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Objective & Hypothesis:Objective: The aim of this study is to classify the magnetoencephalography (MEG) signals with artificial neural network to solve brain activity.
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Key Findings & Data:The features of the signals were extracted by the Riemannian approach and the accuracy of the GRNN was calculated by the 10-fold cross validation technique.
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Conclusion & Impact:Conclusion: GRNN is thought to be a successful alternative to existing methods for classifying MEG signals.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

Objective: The aim of this study is to classify the magnetoencephalography (MEG) signals with artificial neural network to solve brain activity. Methods: The Generalized Regression Neural Network (GRNN) was used to classify MEG signals. The features of the signals were extracted by the Riemannian approach and the accuracy of the GRNN was calculated by the 10-fold cross validation technique. Results: In the study, MEG data recorded from 306 channels belonging to 7 male subjects and 9 female subjects were used. Approximately 588 stimuli were shown to each individual, so the entire data set is composed of 9414 stimuli. Mean specificity, mean sensitivity and mean classification accuracy were obtained 75.43%, 82.57% and 79%, respectively. The classification accuracies obtained by this study and other studies for same MEG dataset were presented comparatively. Conclusion: GRNN is thought to be a successful alternative to existing methods for classifying MEG signals.

Özet

Amaç: Bu çalışmanın amacı, beyin aktivitesini çözmek için Manyetoensefalografi (MEG) sinyallerini yapay sinir ağı ilesınıflandırmaktır.Yöntemler: MEG sinyallerini sınıflandırmak için Genelleştirilmiş Regresyon Sinir Ağı (GRSA) kullanılmıştır.Riemannian yaklaşımı ile sinyallerin öznitelikleri çıkarılmış ve 10 katlı çapraz doğrulama tekniği ile GRSA’nındoğruluğu hesaplanmıştır.Bulgular: Çalışmada 9 kız, 7 erkek bireye ait 306 kanaldan kaydedilen MEG verileri kullanılmıştır. Her bireye yaklaşık588 uyaran gösterilmiştir ve böylece tüm veri seti 9414 uyarandan oluşmaktadır. Ortalama spesifite, ortalamaduyarlılık ve ortalama sınıflandırma doğruluğu sırasıyla %75,43, %82,57 ve %79 olarak elde edilmiştir. Bu çalışma veaynı MEG veri setini kullanan diğer çalışmalar tarafından elde edilen sınıflandırma doğrulukları karşılaştırmalı olaraksunulmuştur.Sonuç: GRSA’nın MEG sinyallerinin sınıflandırılmasında kullanılan mevcut yöntemlere başarılı bir alternatifoluşturduğu düşünülmektedir.

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Cite This Article

ÇETİN, O., TEMURTAŞ, F. (2019). Classification of Magnetoencephalography Signals Regarding Visual Stimuli by Generalized Regression Neural Network. Dicle Tıp Dergisi, Cilt: 46 - Sayı: 1, 18–25. https://ajindex.com/articles/299489

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