Research Article
Görsel Uyaranlara İlişkin Manyetoensefalografi Sinyallerinin Genelleştirilmiş Regresyon Sinir Ağı ile Sınıflandırılması
Abstract
Amaç: Bu çalışmanın amacı, beyin aktivitesini çözmek için
Manyetoensefalografi (MEG) sinyallerini yapay sinir ağı ile sınıflandırmaktır.
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’nın doğruluğu hesaplanmıştır.
Bulgular: Çalışmada 9 kız, 7 erkek bireye ait 306 kanaldan
kaydedilen MEG verileri kullanılmıştır. Her bireye yaklaşık 588 uyaran
gösterilmiştir ve böylece tüm veri seti 9414 uyarandan oluşmaktadır. Ortalama
spesifite, ortalama duyarlılık ve ortalama sınıflandırma doğruluğu sırasıyla
%75,43, %82,57 ve %79 olarak elde edilmiştir. Bu çalışma ve aynı MEG veri
setini kullanan diğer çalışmalar tarafından elde edilen sınıflandırma
doğrulukları karşılaştırmalı olarak sunulmuştur.
Sonuç: GRSA’nın MEG sinyallerinin sınıflandırılmasında
kullanılan mevcut yöntemlere başarılı bir alternatif oluşturduğu
düşünülmektedir.
References
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- 26. Gorur K, Bozkurt MR, Bascil MS, Temurtas F. Glossokinetic potential based tongue-machine interface for 1-D extraction. Australas Phys Eng Sci Med 2018; 41: 379-91.
- 27. Bascil MS, Oztekin H. A study on hepatitis disease diagnosis using probabilistic neural network. Journal of Medical Systems 2012; 36: 1603-6.
Year 2019,
Volume: 46 Issue: 1, 19 - 25, 03.03.2019
Abstract
References
- 1. Bascil MS, Tesneli AY, Temurtas F. A study on analog and digital EEG signal filtering for brain computer ınterfaces (BCI). Electronic Letters on Science&Engineering 2014; 10: 1-10.
- 2. Olivetti E, Kia SM, Avesani P. MEG decoding across subjects. International Workshop on Pattern Recognition in Neuroimaging 2014; doi: 10.1109/PRNI.2014.6858538.
- 3. Caliskan A, Yuksel ME, Badem H, Basturk A. A deep neural network classifier for decoding human brain activity based on Magnetoencephalography. Elektronika ir Elektrotechnika 2017; 23: 63-7.
- 4. Cetin O, Temurtas F. Öğrenmeli vektör kuantalama ile beyin bilgisayar arayüzü üzerine bir çalışma. Sakarya University Journal of Computer and Information Sciences 2018; 1: 1 7.
- 5. Gulbag A, Temurtas F. A study on quantitative classification of binary gas mixture using neural networks and adaptive neuro-fuzzy inference systems. Sensors and Actuators B 2006; 115: 252-62.
- 6. Abadi MK, Subramanian R, Kia SM, Avesani P, Patras I, Sebe N. DECAF: MEG-based multimodal database for decoding affective physiological responses. IEEE Trans. Affective Computing 2015; 6: 209-22.
- 7. Chan AM, Halgren E, Marinkovic K, Cash SS. Decoding word and category-specific spatiotemporal representations from MEG and EEG. NeuroImage 2011; 54: 3028-39.
- 8. Daliri MR. A hybrid method for the decoding of spatial attention using the MEG brain signals. Biomedical Signal Processing and Control 2014; 10: 308-12.
- 9. Kia SM, Pedregosa F, Blumenthal A, Passerini A. Group-level spatio-temporal pattern recovery in MEG decoding using multi-task joint feature learning. Journal of Neuroscience Methods 2017; 285: 97-108.
- 10. Huttunen H, Kauppi JP, Tohka J. Regularized logistic regression for mind reading with parallel validation. ICANN 2011 International Conference on Artificial Neural Networks, Pattern Analysis, Statistical Modelling and Computational Learning2 MEG Mind-Reading 2011; 20-24.
- 11. Kia SM, Vega Pons S, Weisz N, Passerini A. Interpretability of multivariate brain maps in linear brain decoding: Definition, and heuristic quantification in multivariate analysis of MEG time-locked effects. Frontiers in Neuroscience 2017; 10: 619; doi: 10.3389/fnins.2016.00619
- 12. Redcay E, Carlson TA. Rapid neural discrimination of communicative gestures. Social Cognitive and Affective Neuroscience 2015; 10: 545-51.
- 13. Ursulean R, Lazar AM. Detrended cross-correlation analysis of biometric signals used in a new authentication method. Elektronika ir Elektrotechnika 2009; 89: 55-8.
- 14. Raudonis V, Narvydas G, Simutis R. A classification of flash evoked potentials based on artificial neural network. Elektronika ir Elektrotechnika 2008; 81: 31-6.
- 15. Barachant A, Bonnet S, Congedo M, Jutten C. Multiclass brain–computer interface classification by Riemannian geometry. IEEE Trans. Biomedical Engineering 2012; 59: 920-28.
- 16. Yger F, Berar M, Lotte F. Riemannian approaches in brain computer interfaces: a review. IEEE Trans. Neural Systems and Rehabilitation Engineering 2017; 25: 1753-62.
- 17. Henson RN, Wakeman DG, Litvak V, Friston KJ. A parametric empirical Bayesian framework for the EEG/MEG inverse problem: generative models for multi-subject and multi-modal integration. Frontiers in Human Neuroscience 2011; 5: 76.
- 18. Cetin O, Temurtas F, Gulgonul S. An application of multilayer neural network on hepatitis disease diagnosis using approximations of sigmoid activation function. Dicle Medical Journal 2015; 42: 150-7.
- 19. Ulaşabileceği adres: https://www.kaggle.com/c/decoding-the-human-brain
- 20. Moakher M. A differential geometric approach to the geometric mean of symmetric positive-definite matrices. SIAM Journal on Matrix Analysis and Applications 2005; 26: 735-47.
- 21. Ulaşabileceği adres: https://github.com/alexandrebarachant/covariancetoolbox
- 22. Pennec X, Fillard P, Ayache N. A Riemannian framework for tensor computing. International Journal of Computer Vision 2006; 66: 41-66.
- 23. Specht DF. A general regression neural network. IEEE Transactions on Neural Networks 1991; 2: 568-76.
- 24. Specht DF, Shapiro PD. Generalization accuracy of probabilistic neural networks compared with backpropagation networks. IJCNN-91-Seattle International Joint Conference on Neural Networks 1991;887-92; doi: 10.1109/IJCNN.1991.155296.
- 25. Bascil MS, Cetin O, Er O, Temurtas F. Olasılıksal sinir ağının (PNN) parkinson hastalığının teşhisinde kullanılması. Electronic Letters on Science&Engineering 2012; 8: 1-10.
- 26. Gorur K, Bozkurt MR, Bascil MS, Temurtas F. Glossokinetic potential based tongue-machine interface for 1-D extraction. Australas Phys Eng Sci Med 2018; 41: 379-91.
- 27. Bascil MS, Oztekin H. A study on hepatitis disease diagnosis using probabilistic neural network. Journal of Medical Systems 2012; 36: 1603-6.
There are 27 citations in total.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Health Care Administration |
| Journal Section | Research Article |
| Authors | |
| Publication Date | March 3, 2019 |
| Submission Date | July 13, 2018 |
| Published in Issue | Year 2019 Volume: 46 Issue: 1 |