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Journals / Electronic Letters on Science and Engineering / 2018 / Cilt: 14 - Sayı: 1

Classification of Magnetoencephalography Signals by Multilayer and Radial Based Artificial Neural Networks

Çok Katmanlı ve Radyal Tabanlı Yapay Sinir Ağları ile Manyetoensefalografi Sinyallerinin Sınıflandırılması

Onursal ÇETİN
Feyzullah TEMURTAŞ
Pages
32–38
Publication Date
2018-08-16
DOI
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AI Summary & Key Takeaways

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

The performances of proposed classifiers are compared with the results of the previous studies using same dataset.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Magnetoencephalography (MEG) is a neuroimaging technique for recording brain activity, using very sensitive magnetometers.
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Key Findings & Data:In the case of visual stimulation, the relationship between the stimuli and the signal due to the generated mental activity is important for the development of machine learning algorithms.
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Conclusion & Impact:The performances of proposed classifiers are compared with the results of the previous studies using same dataset.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Manyetoensefalografi · Çok Katmanlı Sinir Ağı · Radyal Tabanlı Sinir Ağı

Abstract

Magnetoencephalography (MEG) is a neuroimaging technique for recording brain activity, using very sensitive magnetometers. MEG signals are obtained from neuronal electrical activity and are capable of providing important information for decoding brain activity. In the case of visual stimulation, the relationship between the stimuli and the signal due to the generated mental activity is important for the development of machine learning algorithms. MEG signals have a complex structure due to environmental factors and functional differences arising from brain structures of individuals. It is difficult to get meaningful information from these complex signals. For this reason it is necessary to utilize advanced signal processing techniques. In this study, the successes of multilayer neural network (MLNN) and radial basis neural network (RBNN) have been demonstrated by classifying magnetoencephalography signals through MLNN and RBNN. The performances of proposed classifiers are compared with the results of the previous studies using same dataset.

Özet

Manyetoensefalografi (MEG) çok hassas manyetometreler kullanarak beyin aktivitesini kaydetmek için bir nörogörüntüleme tekniğidir. MEG sinyalleri nöronal elektriksel aktiviteden elde edilir ve beyin aktivitesinin kodunu çözmek için önemli bilgiler sağlayabilir. Görsel uyarım durumunda, uyarıcı ile üretilen zihinsel aktiviteye bağlı sinyal arasındaki ilişki, makine öğrenimi algoritmalarının geliştirilmesi için önemlidir. MEG sinyalleri, çevresel faktörler ve bireylerin beyin yapılarından kaynaklanan fonksiyonel farklılıklar nedeniyle karmaşık bir yapıya sahiptir. Bu karmaşık sinyallerden anlamlı bilgi edinmek zordur. Bu nedenle gelişmiş sinyal işleme tekniklerini kullanmak gereklidir. Bu çalışmada, çok katmanlı sinir ağı (MLNN) ve radyal tabanlı sinir ağının (RBNN) başarıları, MLNN ve RBNN aracılığıyla magnetoensefalografi sinyalleri sınıflandırılarak gösterilmiştir. Önerilen sınıflandırıcıların performansları, aynı veri kümesini kullanan önceki çalışmaların sonuçlarıyla karşılaştırılmıştır.

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Cite This Article

ÇETİN, O., TEMURTAŞ, F. (2018). Classification of Magnetoencephalography Signals by Multilayer and Radial Based Artificial Neural Networks. Electronic Letters on Science and Engineering, Cilt: 14 - Sayı: 1, 32–38. https://ajindex.com/articles/133737

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