Abstract
İnsan beyninin çalışma
mekanizmasını değerlendirmek için yapılan nörolojik çalışmalar, müziğin bu
konuda değerlendirilebilecek önemli bir araç olduğunu göstermektedir. Bu
çalışmada, müzik dinleme görevlerinin, beyin bilgisayar arayüzü (BBA)
sisteminde kullanılabilirliği araştırılmıştır. Müzik görevlerinin diğer
zihinsel ve motor görevlerle sınıflandırma performansları değerlendirilmiştir. Üç
sağlıklı katılımcı ile gerçekleştirilen deneysel çalışmada, yedi farklı görevin
ikili sınıflandırma sonuçları değerlendirilmiştir. Bu görevler, iki farklı müzik türünü dinleme,
rahat durum, zihinden problem çözme, sağ el hareket hayali, sol el hareket
hayali ve A harfi hayali görevleridir. Elde edilen EEG verilerinden Öz bağlanım
(AR) parametreleri, Hjorth parametreleri, güç spektral yoğunluk (PSD)
parametreleri ve PSD+frekans karakteristikleri öznitelik olarak çıkarılmış ve
performansları Destek Vektör Makinesi (DVM), k-En Yakın Komşuluk (k-NN) ve
Yapay Sinir Ağları (ANN) sınıflandırıcıları ile değerlendirilmiştir. Öznitelikler
olarak AR parametreleri kullanılması durumunda, en yüksek sınıflandırma
başarıları %100 DVM ve % 100 ANN olarak
elde edilmiştir. Sınıflandırma başarımları beynin farklı bölümlerini temsil
eden farklı elektrotlar açısından da değerlendirilmiş ve müzik görevlerinin
ayrıştırılmasında C3 kanalının daha başarılı olduğu görülmüştür. Elde edilen
sonuçlara bağlı olarak, müzik dinlenme görevinin beyinde farklı frekanslarda
etki yarattığı ve bu farklılığın tıbbi, askeri ya da e-oyun gibi beyin
bilgisayar ara yüzü uygulamalarında kullanılması önerilmektedir.
References
- Asada, H., Fukuda, Y., Tsunoda, S., Yamaguchi, M., Tonoike, M. (1999) “ Frontal midline theta rhythms reflect alternative activation of prefrontal cortex and anterior cingulated cortex in humans.” Journal of Neurophysiology (50), 324 – 328. doi:10.1016/S0304-3940(99)00679-5
- Anderson, C.W., Devulapalli, S.V., Stolze, A., (1995) Determining mental state from EEG signals using parallel implementations of neural networks. Scientific Programming IOS Press, 4, 3: 171-183. CCC 10.58-9244/95/030171-t:3
- Anderson C.W. ve Sijercic, Z., (1996) Classification of EEG signals from four subjects during five mental tasks. Solving Engineering Problems with Neural Networks Proc. Int.Conf. on Engineering Applications of Neural Networks (EANN’96).
- Alpaydin, E. (2004)``Introduction to Machine Learning'', MIT Press.
- Bashashati, A.ve Fatourechi, M., (2007) A survey of signal processing algorithms in brain-computer interfaces based on electrical brain signals, Journal of Neural Eng., 4, 32-57. doi:10.1088/1741-2560/4/2/R03
- Bhattacharya, J., Petsche, H., Pereda, E.,(2001) “Interdependencies in the spontaneous EEG while listening to music” International Journal of Psychophysiology, Volume 42, Issue 3, November, 287–301. PMID:11812395
- Boser, B. E., Guyon, I. M., ve Vapnik, V. N. (1992, July). A training algorithm for optimal margin classifiers. In Proceedings of the fifth annual workshop on Computational learning theory,144-152. ACM. doi:10.1145/130385.130401
- Dey, A. , Palit, S.K., Bhattacharya, D.K., Tibarewala, D.N., Debraj D.,(2013) “Study of the effect of music on central nervous system through long term analysis of EEG signal in time domain” International Journal of Engineering Sciences & Emerging Technologies, Volume 5, Issue 1, 59-67, Apr. 2013.
- Duda, R. O., Hart, P. E., Stork, D.G.,(2001)Pattern classification, John Wiley & Sons Inc., USA, 654. ISBN: 978-0-471-05669-0
- Durmuş E., Özmen N.G.,(2015)"Beyin Bilgisayar Arayüzü Sistemlerinde Uygun Elektrot Seçimi ve Performans Artırımı,", IEEE 23. Sinyal İşleme ve İletişim Uygulamaları Kurultayı (SIU 2015), Malatya, Türkiye, 16-19 Mayıs 2015, 1142-1145.
- Durmuş E., Sadreddini Z., Özmen N.G.,(2014) "Beyin Bilgisayar Arayüzü Sistemleri İçin Uygun Öznitelik Ve Sınıflandırıcı Seçimi", Otomatik Kontrol Ulusal Toplantısı TOK,, Kocaeli, Türkiye, 11-13 Eylül 2014, 651-656
- Grierson,M. (2008) Composing With Brainwaves: Minimal Trial P300b Recognition as an Indication of Subjective Preference for the Control of a Musical Instrument, Proceedings of the ICMC, Belfast 2008
- Hadjidimitrou S., ve Hadjileontiadis L., (2012), EEG-based discrimination of music appraisal judgements using ZAM time frequency distribution, 8th triennial conf of the European society for the cognition science of music, July 23-28, Greece, 380-381.
- Hamadicharef, B. ; Mufeng Xu ; Aditya, S. (2010) Brain-Computer Interface (BCI) Based Musical Composition, International Conference on Cyberworlds (CW),20-22 Oct. 2010, 282–286, doi: 10.1109/CW.2010.32
- Kwak, N., Choi, C.H., (2002) “Input feature selection for classification problems,” IEEE Transactions on Neural Networks, Vol. 13, No. 1, pp. 143-159, doi: 10.1109/72.977291 ·
- Kristeva, R., Chakarov, V., Schulte-Monting, J., Spreer, J. (2003) Activation of cortical areas in music execution and imagining: a high-resolution EEG study, NeuroImage 20, 1872-1883. pmid:14642497.
- Lal T.N, Schröder, M Hill, N. J., et. al.. (2005) A brain-computer interface with online feedback based on magnetoencephalography, Proceedings of the Int Conf on Machine Learning, 465–472, doi:10.1145/1102351.1102410.
- Lin, Y.P., Wang, C.H., Wu, T.L., Jeng, S.K., Chen, J.H., (2009) EEG-based emotion recognition in music listening: A comparison of schemes for multiclass support vector machine. In ICASSP 2009, 489-492.
- Lotte F, Congedo M, Lécuyer A, Lamarche F, Arnaldi B (2007) A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces, Journal of Neural Engineering, 4, R1-R13. doi:10.1088/1741-2560/4/2/R01
- Makeig, S., Leslie, G., Mullen T., Sarma ,D., Bigdely-Shamlo, N. ve Kothe, C.,(2011) "First demonstration of a musical emotion BCI." Affective Computing and Intelligent Interaction. Springer Berlin Heidelberg,. 487-496. doi: 10.3389/fnhum.2017.00213
- Mühl C, Allison B, Nijholt A, Chanel G (2014) A survey of affective brain computer interfaces: principles, state-of-the-art, and challenges, Brain-Computer Interfaces, 1:2, 66-84, doi: 10.1080/2326263X.2014.912881
- Özmen, N.G., Gümüsel, L.,(2010) Mental and Motor Task Classification by LDA”, MEDICON 2010, IFMBE Proceedings29, pp. 172-175, Chalkidiki, Greece , 28-29 May.
- Özmen, N.G., Gümüsel, L.,(2011) “Discrimination between Mental and Motor Tasks of EEG Signals Using Different Classification Methods”, INISTA ,15-18 June, Istanbul, Turkey.
- Özmen, N.G., Gümüsel, L., (2013) Classification of Real and Imaginary Hand Movements for a BCI Design, IEEE, TSP 2013. 978-1-4799-0404-4/13/$31.00
- Peretz, I., Zatorre, R., (2005) Brain Organization for Music Processing. Annual Review of Psychology, (56),89 – 114. doi:10.1146/56.091103.070225.
- Pfurtscheller,G., Kalcher J., Neuper Ch., et. al. (1996) On-line EEG classification during externally-paced hand movements using a neural network-based classifier, Electroencephalography and Clinical Neurophysiology, 99, 5 ,416–425. doi: 10.1016/S0013-4694(96)95689-8
- Sadreddini Z., Durmuş E., Özmen N.G.,(2014) "EEG Verilerinden Farklı Müzik Türü ve Zihinsel Görevlerin Ayırt Edilmesi", Akıllı sistemlerde Yenilik ve Uygulamaları, ASYU 2014, İzmir, Türkiye, 9-10 Ekim 2014, 44-48.
- Wolpaw, J.R., Birbaumer, N., Heetderks, W.J.et al, (2000) Brain–computer interface technology: A review of the first international meeting. IEEE Trans. Rehab. Eng., 8, 2164-173.
- Wu, J. , Zhang, J., Liu, C., Liu, D., Ding, X, Zhou, C. (2012) “Graph theoretical analysis of EEG functional connectivity during music perception”, Brain Research 1483, 71-81. doi: 10.1016/j.brainres.2012.09.014
- http://muzisyenbeyin.blogspot.com.tr/2013/10/muzigin-vucudumuzdaki-hormonlar-ve.html Erişim Tarihi: 01.02.2016, Konu: Müzik ve Beyin.
Abstract
Neurological studies on human brain show that, music is
an important tool that can be assessed for understanding the mechanism of the
brain. In this study, the availability of music classification for brain
computer interface systems was studied. Moreover, classification performances
of music tasks with other mental and motor tasks are evaluated. An experimental
study was carried out with three different subjects executing seven different
tasks. These tasks are; listening to music, relax, mental arithmetic, imagery right
hand movement, imagery left hand movement and the letter A imagination task.
Autoregressive (AR) parameters, Hjorth parameters, power spectral density (PSD)
values and PSD+ frequency characteristics were extracted as features from the
resulting EEG data. Their classification performances are tested with Support
Vector Machines (SVM), k-nearest neighborhood (k-NN) and Neural Network (ANN)
classifiers. By using AR parameters as features, the highest classification performances
were obtained as 100% SVM and 100% ANN. Classification performances were also
evaluated for different electrodes representing different sections of the brain
and it is observed that, C3 channel has the highest performance for music
tasks. As a result, we can conclude that music tasks affect different
frequencies in the brain, and that difference can be used in different brain
computer interface applications like medical, military or e-gaming applications.
References
- Asada, H., Fukuda, Y., Tsunoda, S., Yamaguchi, M., Tonoike, M. (1999) “ Frontal midline theta rhythms reflect alternative activation of prefrontal cortex and anterior cingulated cortex in humans.” Journal of Neurophysiology (50), 324 – 328. doi:10.1016/S0304-3940(99)00679-5
- Anderson, C.W., Devulapalli, S.V., Stolze, A., (1995) Determining mental state from EEG signals using parallel implementations of neural networks. Scientific Programming IOS Press, 4, 3: 171-183. CCC 10.58-9244/95/030171-t:3
- Anderson C.W. ve Sijercic, Z., (1996) Classification of EEG signals from four subjects during five mental tasks. Solving Engineering Problems with Neural Networks Proc. Int.Conf. on Engineering Applications of Neural Networks (EANN’96).
- Alpaydin, E. (2004)``Introduction to Machine Learning'', MIT Press.
- Bashashati, A.ve Fatourechi, M., (2007) A survey of signal processing algorithms in brain-computer interfaces based on electrical brain signals, Journal of Neural Eng., 4, 32-57. doi:10.1088/1741-2560/4/2/R03
- Bhattacharya, J., Petsche, H., Pereda, E.,(2001) “Interdependencies in the spontaneous EEG while listening to music” International Journal of Psychophysiology, Volume 42, Issue 3, November, 287–301. PMID:11812395
- Boser, B. E., Guyon, I. M., ve Vapnik, V. N. (1992, July). A training algorithm for optimal margin classifiers. In Proceedings of the fifth annual workshop on Computational learning theory,144-152. ACM. doi:10.1145/130385.130401
- Dey, A. , Palit, S.K., Bhattacharya, D.K., Tibarewala, D.N., Debraj D.,(2013) “Study of the effect of music on central nervous system through long term analysis of EEG signal in time domain” International Journal of Engineering Sciences & Emerging Technologies, Volume 5, Issue 1, 59-67, Apr. 2013.
- Duda, R. O., Hart, P. E., Stork, D.G.,(2001)Pattern classification, John Wiley & Sons Inc., USA, 654. ISBN: 978-0-471-05669-0
- Durmuş E., Özmen N.G.,(2015)"Beyin Bilgisayar Arayüzü Sistemlerinde Uygun Elektrot Seçimi ve Performans Artırımı,", IEEE 23. Sinyal İşleme ve İletişim Uygulamaları Kurultayı (SIU 2015), Malatya, Türkiye, 16-19 Mayıs 2015, 1142-1145.
- Durmuş E., Sadreddini Z., Özmen N.G.,(2014) "Beyin Bilgisayar Arayüzü Sistemleri İçin Uygun Öznitelik Ve Sınıflandırıcı Seçimi", Otomatik Kontrol Ulusal Toplantısı TOK,, Kocaeli, Türkiye, 11-13 Eylül 2014, 651-656
- Grierson,M. (2008) Composing With Brainwaves: Minimal Trial P300b Recognition as an Indication of Subjective Preference for the Control of a Musical Instrument, Proceedings of the ICMC, Belfast 2008
- Hadjidimitrou S., ve Hadjileontiadis L., (2012), EEG-based discrimination of music appraisal judgements using ZAM time frequency distribution, 8th triennial conf of the European society for the cognition science of music, July 23-28, Greece, 380-381.
- Hamadicharef, B. ; Mufeng Xu ; Aditya, S. (2010) Brain-Computer Interface (BCI) Based Musical Composition, International Conference on Cyberworlds (CW),20-22 Oct. 2010, 282–286, doi: 10.1109/CW.2010.32
- Kwak, N., Choi, C.H., (2002) “Input feature selection for classification problems,” IEEE Transactions on Neural Networks, Vol. 13, No. 1, pp. 143-159, doi: 10.1109/72.977291 ·
- Kristeva, R., Chakarov, V., Schulte-Monting, J., Spreer, J. (2003) Activation of cortical areas in music execution and imagining: a high-resolution EEG study, NeuroImage 20, 1872-1883. pmid:14642497.
- Lal T.N, Schröder, M Hill, N. J., et. al.. (2005) A brain-computer interface with online feedback based on magnetoencephalography, Proceedings of the Int Conf on Machine Learning, 465–472, doi:10.1145/1102351.1102410.
- Lin, Y.P., Wang, C.H., Wu, T.L., Jeng, S.K., Chen, J.H., (2009) EEG-based emotion recognition in music listening: A comparison of schemes for multiclass support vector machine. In ICASSP 2009, 489-492.
- Lotte F, Congedo M, Lécuyer A, Lamarche F, Arnaldi B (2007) A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces, Journal of Neural Engineering, 4, R1-R13. doi:10.1088/1741-2560/4/2/R01
- Makeig, S., Leslie, G., Mullen T., Sarma ,D., Bigdely-Shamlo, N. ve Kothe, C.,(2011) "First demonstration of a musical emotion BCI." Affective Computing and Intelligent Interaction. Springer Berlin Heidelberg,. 487-496. doi: 10.3389/fnhum.2017.00213
- Mühl C, Allison B, Nijholt A, Chanel G (2014) A survey of affective brain computer interfaces: principles, state-of-the-art, and challenges, Brain-Computer Interfaces, 1:2, 66-84, doi: 10.1080/2326263X.2014.912881
- Özmen, N.G., Gümüsel, L.,(2010) Mental and Motor Task Classification by LDA”, MEDICON 2010, IFMBE Proceedings29, pp. 172-175, Chalkidiki, Greece , 28-29 May.
- Özmen, N.G., Gümüsel, L.,(2011) “Discrimination between Mental and Motor Tasks of EEG Signals Using Different Classification Methods”, INISTA ,15-18 June, Istanbul, Turkey.
- Özmen, N.G., Gümüsel, L., (2013) Classification of Real and Imaginary Hand Movements for a BCI Design, IEEE, TSP 2013. 978-1-4799-0404-4/13/$31.00
- Peretz, I., Zatorre, R., (2005) Brain Organization for Music Processing. Annual Review of Psychology, (56),89 – 114. doi:10.1146/56.091103.070225.
- Pfurtscheller,G., Kalcher J., Neuper Ch., et. al. (1996) On-line EEG classification during externally-paced hand movements using a neural network-based classifier, Electroencephalography and Clinical Neurophysiology, 99, 5 ,416–425. doi: 10.1016/S0013-4694(96)95689-8
- Sadreddini Z., Durmuş E., Özmen N.G.,(2014) "EEG Verilerinden Farklı Müzik Türü ve Zihinsel Görevlerin Ayırt Edilmesi", Akıllı sistemlerde Yenilik ve Uygulamaları, ASYU 2014, İzmir, Türkiye, 9-10 Ekim 2014, 44-48.
- Wolpaw, J.R., Birbaumer, N., Heetderks, W.J.et al, (2000) Brain–computer interface technology: A review of the first international meeting. IEEE Trans. Rehab. Eng., 8, 2164-173.
- Wu, J. , Zhang, J., Liu, C., Liu, D., Ding, X, Zhou, C. (2012) “Graph theoretical analysis of EEG functional connectivity during music perception”, Brain Research 1483, 71-81. doi: 10.1016/j.brainres.2012.09.014
- http://muzisyenbeyin.blogspot.com.tr/2013/10/muzigin-vucudumuzdaki-hormonlar-ve.html Erişim Tarihi: 01.02.2016, Konu: Müzik ve Beyin.
Details
| Subjects | Engineering |
|---|---|
| Journal Section | Research Articles |
| Authors | |
| Publication Date | August 20, 2017 |
| Submission Date | February 12, 2016 |
| Acceptance Date | May 3, 2017 |
| Published in Issue | Year 2017 Volume: 22 Issue: 2 |
Cite
Cited By
DETECTION OF ALZHEIMER'S DISEASE FROM ELECTROENCEPHALOGRAPHY (EEG) SIGNALS USING MULTITAPER AND ENSEMBLE LEARNING METHODS
Uludağ University Journal of The Faculty of Engineering
https://doi.org/10.17482/uumfd.1142345
ESTIMATION OF PAIN THRESHOLD FROM EEG SIGNALS OF SUBJECTS IN PHYSICAL THERAPY USING LONG-SHORT-TERM MEMORY DEEP LEARNING MODEL
Uludağ University Journal of The Faculty of Engineering
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https://doi.org/10.17482/uumfd.883100
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