Görsel Uyaranlara İlişkin Manyetoensefalografi Sinyallerinin Genelleştirilmiş Regresyon Sinir Ağı ile Sınıflandırılması
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 a...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Dicle University Medical School
2019-03-01
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| Series: | Dicle Medical Journal |
| Subjects: | |
| Online Access: | http://diclemedj.org/upload/sayi/72/Dicle%20Med%20J-03563.pdf |
| Summary: | 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. |
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| ISSN: | 1300-2945 1308-9889 |