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Öğrenmeli Vektör Kuantalama ile Beyin Bilgisayar Arayüzü Üzerine Bir Çalışma

Year 2018, Volume: 1 Issue: 2, 1 - 7, 01.08.2018

Onursal Çetin , Feyzullah Temurtaş

Abstract

Beyin-bilgisayar arayüzü (BCI) insan beyni ve bilgisayar
arasında iletişim ve kontrol sağlayabilir. Beyin sinyallerinin saptanması, bu
sistemler için en temel düzeydir. Manyetoensefalografi (MEG) beyin aktivitesini
çözmek için invazif olmayan bir görüntüleme tekniğidir. MEG sinyalleri
karmaşıktır ve çevresel olaylardan ve beynin fonksiyonel farklılıklarından
kolayca etkilenebilir. BCI sistemleri için bu karmaşık sinyallerden bilgi almak
zordur. Bu nedenle, bilgiyi anlamlı kılmak için ileri sinyal işleme teknikleri
gereklidir. Bu çalışmada, manyetoensefalografi sinyallerini öğrenmeli vektör kuantalama
(LVQ) ile sınıflandırarak LVQ algoritmasının başarısı ortaya konulmuştur.
Sınıflandırma doğruluğu 10 kat çapraz doğrulama ile elde edilmiştir. Önerilen
sınıflandırıcının performansı, MEG'e odaklanan ve aynı veri setini kullanan
önceki yöntemler ile karşılaştırılmıştır.

Keywords

Beyin-bilgisayar arayüzü manyetoensefalografi öğrenmeli vektör kuantalama sınıflandırma

References

  • Abadi M. K., Subramanian R., Kia S. M., Avesani P., Patras I., Sebe N. (2015). “DECAF: MEG-based multimodal database for decoding affective physiological responses”, IEEE Trans. Affective Computing, 6(3), 209–222.
  • Alkım E., Gürbüz E., Kılıç E. (2012). “A fast and adaptive automated disease diagnosis method with an innovative neural network model”, Neural Networks, 33, 88–96.
  • Barachant A. “Covariance toolbox”. https://github.com/alexandrebarachant/covariancetoolbox [Accessed: 05.05.2018]
  • Barachant A., Bonnet S., Congedo M., Jutten C. (2012). “Multiclass brain–computer interface classification by Riemannian geometry”, IEEE Trans. Biomedical Engineering, 59(4), 920–928.
  • Bascil M. S., Cetin O., Er O., Temurtas F. (2012). “Olasılıksal Sinir Ağının(PNN) Parkinson Hastalığının Teşhisinde Kullanılması” Electronic Letters on Science&Engineering, 8(1), 1–10.
  • Bascil M. S., Oztekin H. (2012). “A Study on Hepatitis Disease Diagnosis Using Probabilistic Neural Network”, Journal of Medical Systems, 36(3), 1603–1606.
  • Bascil M. S., Tesneli A. Y., Temurtas F. (2014). “A study on Analog and Digital EEG Signal Filtering for Brain Computer Interfaces (BCI)”, Electronic Letters on Science&Engineering, 10(1), 1–10.
  • Bradberry T. J., Rong F., Contreras-Vidal J. L. (2009). “Decoding center-out hand velocity from MEG signals during visuomotor adaptation”, Neuroimage, 47(4), 1691–1700.
  • Caliskan A. (2017). “Derin sinir ağının sınıflandırma başarımının artırılması için yeni bir eğitim stratejisi geliştirilmesi ve biyomedikal veri setleri üzerinde test edilmesi”, Erciyes Üniversitesi Fen Bilimleri Enstitüsü Biyomedikal Mühendisliği Anabilim Dalı, Doktora Tezi.
  • Caliskan A., Yuksel M. E., Badem H., Basturk A. (2017). “A Deep Neural Network Classifier for Decoding Human Brain Activity Based on Magnetoencephalography” Elektronika ir Elektrotechnika, 23(2), 63–67
  • Cecotti H. (2016). “Single-trial detection with magnetoencephalography during a dual-rapid serial visual presentation task”, IEEE Trans. Biomedical Engineering, 63(1), 220–227.
  • Cetin O., Temurtas F., Gulgonul S. (2015). “An application of multilayer neural network on hepatitis disease diagnosis using approximations of sigmoid activation function” Dicle Medical Journal, 42(2), 150–157.
  • Chan A. M., Halgren E., Marinkovic K., Cash S. S. (2011). “Decoding word and category-specific spatiotemporal representations from MED and EEG”, NeuroImage, 54(4), 3028–3039.
  • Daliri M. R. (2014). “A hybrid method for the decoding of spatial attention using the MEG brain signals”, Biomedical Signal Processing and Control, 10, 308–312.
  • “DecMeg2014-Decoding the Human Brain”. https://www.kaggle.com/c/decoding-the-human-brain [Accessed: 05.05.2018] Delen D., Walker G., Kadam A. (2005). “Predicting breast cancer survivability: A comparison of three data mining methods”, Artificial Intelligence in Medicine, 34(2), 113–127.
  • Er O., Yumusak N., Temurtas F. (2010). “Chest diseases diagnosis using artificial neural networks” Expert Systems with Applications, 37(12), 7648–7655.
  • Gorur K., Bozkurt M. R., Bascil M. S., Temurtas F. (2018). “Glossokinetic potential based tongue-machine interface for 1-D extraction”, Australas Phys Eng Sci Med. doi: 10.1007/s13246-018-0635-x.
  • Gulbag A., Temurtas F. (2006). “A study on quantitative classification of binary gas mixture using neural networks and adaptive neuro-fuzzy inference systems”, Sensors and Actuators B, 115(1), 252–262.
  • Hamalainen M., Hari R., Ilmoniemi R. J., Knuutila J., Lounasmaa O. V. (1993). “Magnetoencephalography theory, instrumentation, and applications to noninvasive studies of the working human brain”, Reviews of Modern Physics, 65(2), 413–497.
  • Henson R. N., Wakeman D. G., Litvak V., Friston K. J. (2011). “A parametric empirical Bayesian framework for the EEG/MEG inverse problem: generative models for multi-subject and multi-modal integration”, Frontiers in Human Neuroscience, 5, p. 76.
  • Huttunen H., Kauppi J. P., Tohka J. (2011). “Regularized logistic regression for mind reading with parallel validation”, 20–24, Aalto-Yliopisto. Kamitani Y., Tong F. (2005). “Decoding the visual and subjective contents of the human brain”, Nature Neuroscience, 8(5), 679–685.
  • Kay K. N., Naselaris T., Prenger R. J., Gallant J. L. (2008).“Identifying natural images from human brain activity”, Nature, 452(7185), 352–355. Kia S. M., Pedregosa F., Blumenthal A., Passerini A. (2017). “Group-level spatio-temporal pattern recovery in MEG decoding using multi-task joint feature learning”, Journal of Neuroscience Methods, 285, 97–108.
  • Kia S. M., Vega Pons S.,Weisz N., Passerini A. (2017). “Interpretability of Multivariate Brain Maps in Linear Brain Decoding: Definition, and Heuristic Quantification in Multivariate Analysis of MEG Time-Locked Effects” Frontiers in Neuroscience, 10, 619, doi: 10.3389/fnins.2016.00619
  • Kohonen T. (1990). “Improved versions of learning vector quantization”, In Proceedings of the IEEE international joint conference on neural networks, pp. 545–550, New York.
  • Kohonen, T. (1990). “The self-organising map”, In Proceedings of the IEEE, 78(9), 1464–1480.
  • Kohonen T., Bama G., Chrisley R. (1988). “Statistical pattern recognition with neural networks benchmarking studies”, in Proceeding of the International Joint Conference on Neural Networks, 1, SanDiago, California, 61–68.
  • Moakher M. (2005). “A differential geometric approach to the geometric mean of symmetric positive-definite matrices”, SIAM Journal on Matrix Analysis and Applications, 26(3), 735–747.
  • Olivetti E., Kia S. M., Avesani P. (2014). “MEG decoding across subjects”, in Proc. Int. Workshop on Pattern Recognition in Neuroimaging, Tubingen, doi: 10.1109/PRNI.2014.6858538
  • Pennec X., Fillard P., Ayache N. (2006). “A Riemannian framework for tensor computing”, International Journal of Computer Vision, 66(1), 41–66.
  • Raudonis V., Narvydas G., Simutis R. (2008). “A classification of flash evoked potentials based on artificial neural network”, Elektronika ir Elektrotechnika, 81(1), 31–36.
  • Redcay E., Carlson T. A. (2015). “Rapid neural discrimination of communicative gestures”, Social Cognitive and Affective Neuroscience, 10(4), 545–551.
  • Temurtas F. (2009). “A comparative study on thyroid disease diagnosis using neural networks”, Expert Systems with Applications, 36(1), 944–949.
  • Ursulean R., Lazar A. M. (2009). “Detrended cross-correlation analysis of biometric signals used in a new authentication method”, Elektronika ir Elektrotechnika, 89(1), 55–58.
  • Wang J., Pohlmeyer E., Hanna B., Jiang Y. G., Sajda P., Chang S. F. (2009). “Brain state decoding for rapid image retrieval”, in Proc. 17th ACM Int. Conf. Multimedia, 945–954.
  • Yger F., Berar M., Lotte F. (2017). “Riemannian approaches in brain computer interfaces: a review”, IEEE Trans. Neural Systems and Rehabilitation Engineering, 25(10), 1753–1762.

A Study on Brain Computer Interface using Learning Vector Quantization

Year 2018, Volume: 1 Issue: 2, 1 - 7, 01.08.2018

Onursal Çetin , Feyzullah Temurtaş

Abstract

Brain-computer interface (BCI) can provide communication and
control between the human brain and a computer. Detection of brain signals is
the most basic level for these systems. Magnetoencephalography (MEG) is a
non-invasive neuroimaging technique for decoding brain activity. MEG signals
are complicated and can be easily affected by environmental events and
functional differences of the brain. It is difficult to get information from
these complex signals for BCI systems. Therefore, advanced signal processing
techniques are required to make the information meaningful. In this study, the
success of learning vector quantization (LVQ) algorithm has been put forward by
classifying magnetoencephalography signals through LVQ. Classification accuracy
is obtained via 10-fold cross validation. The performance of proposed
classifier is compared with the results of the previous methods reported
focusing on MEG and using same dataset.

Keywords

Brain-computer interface magnetoencephalography learning vector quantization classification

References

  • Abadi M. K., Subramanian R., Kia S. M., Avesani P., Patras I., Sebe N. (2015). “DECAF: MEG-based multimodal database for decoding affective physiological responses”, IEEE Trans. Affective Computing, 6(3), 209–222.
  • Alkım E., Gürbüz E., Kılıç E. (2012). “A fast and adaptive automated disease diagnosis method with an innovative neural network model”, Neural Networks, 33, 88–96.
  • Barachant A. “Covariance toolbox”. https://github.com/alexandrebarachant/covariancetoolbox [Accessed: 05.05.2018]
  • Barachant A., Bonnet S., Congedo M., Jutten C. (2012). “Multiclass brain–computer interface classification by Riemannian geometry”, IEEE Trans. Biomedical Engineering, 59(4), 920–928.
  • Bascil M. S., Cetin O., Er O., Temurtas F. (2012). “Olasılıksal Sinir Ağının(PNN) Parkinson Hastalığının Teşhisinde Kullanılması” Electronic Letters on Science&Engineering, 8(1), 1–10.
  • Bascil M. S., Oztekin H. (2012). “A Study on Hepatitis Disease Diagnosis Using Probabilistic Neural Network”, Journal of Medical Systems, 36(3), 1603–1606.
  • Bascil M. S., Tesneli A. Y., Temurtas F. (2014). “A study on Analog and Digital EEG Signal Filtering for Brain Computer Interfaces (BCI)”, Electronic Letters on Science&Engineering, 10(1), 1–10.
  • Bradberry T. J., Rong F., Contreras-Vidal J. L. (2009). “Decoding center-out hand velocity from MEG signals during visuomotor adaptation”, Neuroimage, 47(4), 1691–1700.
  • Caliskan A. (2017). “Derin sinir ağının sınıflandırma başarımının artırılması için yeni bir eğitim stratejisi geliştirilmesi ve biyomedikal veri setleri üzerinde test edilmesi”, Erciyes Üniversitesi Fen Bilimleri Enstitüsü Biyomedikal Mühendisliği Anabilim Dalı, Doktora Tezi.
  • Caliskan A., Yuksel M. E., Badem H., Basturk A. (2017). “A Deep Neural Network Classifier for Decoding Human Brain Activity Based on Magnetoencephalography” Elektronika ir Elektrotechnika, 23(2), 63–67
  • Cecotti H. (2016). “Single-trial detection with magnetoencephalography during a dual-rapid serial visual presentation task”, IEEE Trans. Biomedical Engineering, 63(1), 220–227.
  • Cetin O., Temurtas F., Gulgonul S. (2015). “An application of multilayer neural network on hepatitis disease diagnosis using approximations of sigmoid activation function” Dicle Medical Journal, 42(2), 150–157.
  • Chan A. M., Halgren E., Marinkovic K., Cash S. S. (2011). “Decoding word and category-specific spatiotemporal representations from MED and EEG”, NeuroImage, 54(4), 3028–3039.
  • Daliri M. R. (2014). “A hybrid method for the decoding of spatial attention using the MEG brain signals”, Biomedical Signal Processing and Control, 10, 308–312.
  • “DecMeg2014-Decoding the Human Brain”. https://www.kaggle.com/c/decoding-the-human-brain [Accessed: 05.05.2018] Delen D., Walker G., Kadam A. (2005). “Predicting breast cancer survivability: A comparison of three data mining methods”, Artificial Intelligence in Medicine, 34(2), 113–127.
  • Er O., Yumusak N., Temurtas F. (2010). “Chest diseases diagnosis using artificial neural networks” Expert Systems with Applications, 37(12), 7648–7655.
  • Gorur K., Bozkurt M. R., Bascil M. S., Temurtas F. (2018). “Glossokinetic potential based tongue-machine interface for 1-D extraction”, Australas Phys Eng Sci Med. doi: 10.1007/s13246-018-0635-x.
  • Gulbag A., Temurtas F. (2006). “A study on quantitative classification of binary gas mixture using neural networks and adaptive neuro-fuzzy inference systems”, Sensors and Actuators B, 115(1), 252–262.
  • Hamalainen M., Hari R., Ilmoniemi R. J., Knuutila J., Lounasmaa O. V. (1993). “Magnetoencephalography theory, instrumentation, and applications to noninvasive studies of the working human brain”, Reviews of Modern Physics, 65(2), 413–497.
  • Henson R. N., Wakeman D. G., Litvak V., Friston K. J. (2011). “A parametric empirical Bayesian framework for the EEG/MEG inverse problem: generative models for multi-subject and multi-modal integration”, Frontiers in Human Neuroscience, 5, p. 76.
  • Huttunen H., Kauppi J. P., Tohka J. (2011). “Regularized logistic regression for mind reading with parallel validation”, 20–24, Aalto-Yliopisto. Kamitani Y., Tong F. (2005). “Decoding the visual and subjective contents of the human brain”, Nature Neuroscience, 8(5), 679–685.
  • Kay K. N., Naselaris T., Prenger R. J., Gallant J. L. (2008).“Identifying natural images from human brain activity”, Nature, 452(7185), 352–355. Kia S. M., Pedregosa F., Blumenthal A., Passerini A. (2017). “Group-level spatio-temporal pattern recovery in MEG decoding using multi-task joint feature learning”, Journal of Neuroscience Methods, 285, 97–108.
  • Kia S. M., Vega Pons S.,Weisz N., Passerini A. (2017). “Interpretability of Multivariate Brain Maps in Linear Brain Decoding: Definition, and Heuristic Quantification in Multivariate Analysis of MEG Time-Locked Effects” Frontiers in Neuroscience, 10, 619, doi: 10.3389/fnins.2016.00619
  • Kohonen T. (1990). “Improved versions of learning vector quantization”, In Proceedings of the IEEE international joint conference on neural networks, pp. 545–550, New York.
  • Kohonen, T. (1990). “The self-organising map”, In Proceedings of the IEEE, 78(9), 1464–1480.
  • Kohonen T., Bama G., Chrisley R. (1988). “Statistical pattern recognition with neural networks benchmarking studies”, in Proceeding of the International Joint Conference on Neural Networks, 1, SanDiago, California, 61–68.
  • Moakher M. (2005). “A differential geometric approach to the geometric mean of symmetric positive-definite matrices”, SIAM Journal on Matrix Analysis and Applications, 26(3), 735–747.
  • Olivetti E., Kia S. M., Avesani P. (2014). “MEG decoding across subjects”, in Proc. Int. Workshop on Pattern Recognition in Neuroimaging, Tubingen, doi: 10.1109/PRNI.2014.6858538
  • Pennec X., Fillard P., Ayache N. (2006). “A Riemannian framework for tensor computing”, International Journal of Computer Vision, 66(1), 41–66.
  • Raudonis V., Narvydas G., Simutis R. (2008). “A classification of flash evoked potentials based on artificial neural network”, Elektronika ir Elektrotechnika, 81(1), 31–36.
  • Redcay E., Carlson T. A. (2015). “Rapid neural discrimination of communicative gestures”, Social Cognitive and Affective Neuroscience, 10(4), 545–551.
  • Temurtas F. (2009). “A comparative study on thyroid disease diagnosis using neural networks”, Expert Systems with Applications, 36(1), 944–949.
  • Ursulean R., Lazar A. M. (2009). “Detrended cross-correlation analysis of biometric signals used in a new authentication method”, Elektronika ir Elektrotechnika, 89(1), 55–58.
  • Wang J., Pohlmeyer E., Hanna B., Jiang Y. G., Sajda P., Chang S. F. (2009). “Brain state decoding for rapid image retrieval”, in Proc. 17th ACM Int. Conf. Multimedia, 945–954.
  • Yger F., Berar M., Lotte F. (2017). “Riemannian approaches in brain computer interfaces: a review”, IEEE Trans. Neural Systems and Rehabilitation Engineering, 25(10), 1753–1762.
There are 35 citations in total.

Details

Primary Language Turkish
Subjects Computer Software
Journal Section Articles
Authors

Onursal Çetin

Feyzullah Temurtaş

Publication Date August 1, 2018
Submission Date May 23, 2018
Acceptance Date June 6, 2018
Published in Issue Year 2018Volume: 1 Issue: 2

Cite

IEEE O. Çetin and F. Temurtaş, “Öğrenmeli Vektör Kuantalama ile Beyin Bilgisayar Arayüzü Üzerine Bir Çalışma”, SAUCIS, vol. 1, no. 2, pp. 1–7, 2018.
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