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

Year 2018, Volume: 14 Issue: 1, 32 - 38, 16.08.2018

Onursal Çetin , Feyzullah Temurtaş

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.

Keywords

Magnetoencephalography, Multilayer Neural Network, Radial Basis Neural Network

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

Abstract

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. 

Keywords

Manyetoensefalografi, Çok Katmanlı Sinir Ağı, Radyal Tabanlı Sinir Ağı

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