EEG SİNYALLERİNİ KULLANARAK 2B VE 3B HİBRİT BİR VİDEONUN AYRINTILI KANAL/LOB ANALİZİ

Abstract

İnsan gözünün yapısına göre 2 Boyutlu (2B) ve 3B parçalardan oluşan video analizinde ani geçiş ve sabit durumların değerlendirilmesi önemlidir. Bu çalışmada, insan beyin sinyallerinin güç spektrum yoğunluğu (GSY), 2B ve 3B hibrit video izleme sonucunda analiz için dikkate alınmıştır. İnsanların yorulunca derinlik algısının kaybettiğini daha önceki çalışmalarımızda iddia etmiştik. Bu çalışmada, rastgele 2B ve 3B parçalardan oluşan, tek akışlı bir anaglif video sağlanmıştır. 2B ve 3B hibrit video çalışmasında, beyin sinyal analizinde kısa zamanlı Fourier dönüşümüne (KZFD) dayalı GSY ve spektrogram adı verilen görsel temsil kullanılmıştır. Tüm EEG frekans bantları test edildikten sonra, spektrogram çizelgesindeki 2B ve 3B parçaların karşılaştırılmasında GSY farkı dikkate alınarak, delta bandı baskın bant olarak seçilmiştir. Bu banttan çıkarılan öznitelikler iki popüler sınıflandırıcı tarafından sınıflandırılmıştır. Bunlar destek vektör makinesi (DVM) ve doğrusal ayırma analizi (DAA) algoritmalarıdır. Sonuç olarak, frontal ve temporal loblar, 2B ve 3B geçişlerin sınıflandırılmasında diğer loblara göre daha iyi sonuçlar göstermektedir. Öznitelik çıkarma yöntemi olarak kullanılan istatistiksel fonksiyonlar ve Hjorth parametreleri sonucunda DVM ve DAA algoritmaları için sınıflandırma başarısı sırasıyla %68 ve %79 olarak hesaplanmıştır.

Keywords

• 2B ve 3B
• EEG
• Kararlı-durum
• Öznitelik çıkarma
• Sınıflandırma

Detailed Channel/Lob Analysis of a 2D and 3D Hybrid Video Using EEG Signal

Abstract

It is important to evaluate sudden transition and steady-states in video analysis consisting of 2 dimensional (2D) and 3D tracks, regarding the human eye structure. In this study, the power spectrum density (PSD) of the human brain signals was taken into consideration for analysis as a result of a 2D and 3D hybrid video watching. We claimed in our previous studies that people lose their depth perception when they get tired. In this study, a single stream anaglyph video consisting of random 2D and 3D tracks is provided. In 2D and 3D hybrid video study, PSD based on short-time Fourier transform (STFT) and visual representation called spectrogram were used in brain signal analysis. After all EEG frequency bands have been tested, the delta band has been chosen as the dominant band, taking into account the difference of PSD in the comparison of 2D and 3D parts in the spectrogram chart. Extracted features from this band were classified by two popular classifiers. These are support vector machine (SVM) and Linear discriminant analysis (LDA) algorithms. Consequently, the frontal and temporal lobes show better results in the classification of 2D and 3D transitions than other lobes. As a result of statistical functions and Hjorth parameters used as feature extraction methods, classification success for SVM and LDA algorithms was computed as 68 % and 79 %, respectively.

Keywords

• EEG
• 2D and 3D
• Steady-state
• Classification
• Feature extraction

References

1. Ahmed, S.M., and Abbas, S.N., 2015, “A New EEG Acquisition Protocol for Biometric Identification Using Eye Blinking Signals”, International Journal of Intelligent Systems and Applications, Sayı 7, ss. 48.
2. Al-Qazzaz, N.K., Ali, S.H.B.M., Ahmad, S.A., Chellappan, K., Islam, M.S., and Escudero, J., 2014, “Role of EEG as biomarker in the early detection and classification of dementia”, TheScientificWorldJournal, Sayı 2014, ss. 906038.
3. Belyavin, A., and Wright, N.A., 1987, “Changes in electrical activity of the brain with vigilance” Electroencephalography and Clinical Neurophysiology, Sayı 66, ss. 137–144.
4. Chen, C., Li, K., Wu, Q., Wang, H., Qian, Z., and Sudlow, G., 2013, “EEG-based detection and evaluation of fatigue caused by watching 3DTV”, Displays, Sayı 34, ss. 81–88.
5. Chen, C., Wang, J., Li, K., Wu, Q., Wang, H., Qian, Z., and Gu, N., 2014, “Assessment visual fatigue of watching 3DTV using EEG power spectral parameters”, Displays, Sayı 35, ss. 266–272.
6. Coyle, D., Prasad, G., and Mcginnity, T.M., 2005, “A Time-Frequency Approach to Feature Extraction for a Brain-Computer Interface with a Comparative Analysis of Performance Measures”, 3141–3151 ss.
7. Fischmeister, F.P.S., and Bauer, H., 2006, “Neural correlates of monocular and binocular depth cues based on natural images”, A LORETA analysis: Vision Research, Sayı 46, ss. 3373–3380.
8. Forrester, J. V., Dick, A.D., McMenamin, P.G., Roberts, F., Pearlman, E., Forrester, J. V., Dick, A.D., McMenamin, P.G., Roberts, F., and Pearlman, E., 2016, “Physiology of vision and the visual system,” The Eye, ss. 269-337.e2.

9. Han, Y., Lin, H.Y., and Chen, C., 2017, “SP-3 Visual Fatigue for Laser-Projection Light-Field 3D Display in Contrast with 2D Display”, 2017 24th International Workshop on Active-Matrix Flatpanel Displays and Devices (AM-FPD, ss. 9–12.
10. Harris, F.J. (1987). Multirate FIR Filters for Interpolating and Desampling. In Handbook of Digital Signal Processing, (Elsevier), ss. 173–287.
11. Hlawatsch, F., and Boudreaux-Bartels, G.F., 1992, “Linear and quadratic time-frequency signal representations”, IEEE Signal Processing Magazine, Sayı 9, ss. 21–67.
12. Hosťovecký, M., and B, B., 2017, “Brain activity: beta wave analysis of 2D and 3D serious games using EEG”, JAMSI, Sayı 13.
13. Houmani, N., Vialatte, F., Gallego-Jutglà, E., Dreyfus, G., Nguyen-Michel, V.-H., Mariani, J., and Kinugawa, K., 2018, “Diagnosis of Alzheimer’s disease with Electroencephalography in a differential framework”, PLOS ONE, Sayı 13, ss. e0193607.
14. Idoo, (2019, December 30). Easiest Video Editing Software Free Download, (n.d.). http://www.idooeditor.com/ IQmango, (2019, December 30,). Download Free 3D Video Converter - Convert 2D to 3D | IQmango Free Software, (n.d.). http://iqmango.com/3DVideo_Converter.html
15. Jap, B.T., Lal, S., Fischer, P., and Bekiaris, E., 2009, “Using EEG spectral components to assess algorithms for detecting fatigue”, Expert Systems with Applications, Sayı 36, ss. 2352–2359.
16. Jeong, H.-G., Ko, Y.-H., Han, C., Oh, S.-Y., Park, K.W., Kim, T., and Ko, D., 2015, “The impact of 3D and 2D TV watching on neurophysiological responses and cognitive functioning in adults”, The European Journal of Public Health, Sayı 25, ss. 1047–1052.
17. Jeong, J., 2004, “EEG dynamics in patients with Alzheimer’s disease”, Clinical Neurophysiology, Sayı 115, ss. 1490–1505.
18. Kayikcioglu, T., Maleki, M., and Eroglu, K., 2015, “Fast and accurate PLS-based classification of EEG sleep using single channel data”, Expert Systems with Applications, Sayı 42, ss. 7825–7830.
19. Khairuddin, H.R., Malik, A.S., Member, S., Mumtaz, W., Kamel, N., Member, S., and Member, L.X., 2013, “Analysis of EEG Signals Regularity in Adults during Video Game Play in 2D and 3D”, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), ss. 2064–2067.
20. Kim, J., Kim, Y., Hong, J., Park, G., Hong, K., Min, S.-W., and Lee, B., 2011, “A full-color anaglyph three-dimensional display system using active color filter glasses”, Journal of Information Display, Sayı 12, ss. 37–41.
21. Kim, S., and Kim, D., 2012, In press, Differences in the Brain Waves of 3D and 2 . 5D Motion Picture Viewers: ArXiv Preprint ArXiv:1210.2147,.
22. Kober, S.E., Kurzmann, J., and Neuper, C., 2012, “Cortical correlate of spatial presence in 2D and 3D interactive virtual reality: An EEG”, study: International Journal of Psychophysiology, Sayı 83, ss. 365–374.
23. Lenc, T., Keller, P.E., Varlet, M., and Nozaradan, S., 2018, “Neural tracking of the musical beat is enhanced by low-frequency sounds”, Proceedings of the National Academy of Sciences, Sayı 115, ss. 8221–8226.
24. Li, X., Chen, X., Yan, Y., Wei, W., Wang, Z., Li, X., Chen, X., Yan, Y., Wei, W., and Wang, Z.J., 2014, “Classification of EEG Signals Using a Multiple Kernel Learning Support Vector Machine”, Sensors, Sayı 14, ss. 12784–12802.
25. Lianyang Li, Pagnotta, M.F., Arakaki, X., Tran, T., Strickland, D., Harrington, M., Zouridakis, G., Li, L., Pagnotta, M.F., Arakaki, X., Tran, T., Strickland, D., Harrington, M., Zouridakis, G., Member, S., and Subjects, A., 2015, “Brain Activation Profiles in mTBI : Evidence from Combined Resting-State EEG and MEG Activity”, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), ss. 6963–6966.
26. Lin, Y.-P.P., Wang, C.-H.H., Wu, T.-L.L., Jeng, S.-K.K., and Chen, J.-H.H., 2008, “Support vector machine for EEG signal classification during listening to emotional music”, ss. 127–130.
27. Malik, A.S., Khairuddin, R.N.H.R., Amin, H.U., Smith, M.L., Kamel, N., Abdullah, J.M., Fawzy, S.M., and Shim, S., 2015, “EEG based evaluation of stereoscopic 3D displays for viewer discomfort”, BioMedical Engineering OnLine, Sayı 14, ss. 21.
28. Manshouri, N., and Kayikcioglu, T., 2019, “A Comprehensive Analysis of 2D&3D Video Watching of EEG Signals by Increasing PLSR and SVM Classification Results”, The Computer Journal,.
29. Manshouri, N., Maleki, M., and Kayıkçıoğlu, T., 2017, “Classification of Human Vision Discrepancy during Watching 2D and 3D Movies Based on EEG Signals”, International Journal of Computer Science and Information Security, Sayı 15, ss. 430–436.
30. Manshouri, N., Maleki, M., and Kayikcioglu, T., 2020, “An EEG-based stereoscopic research of the PSD differences in pre and post 2D&3D movies watching”, Biomedical Signal Processing and Control, Sayı 55.
31. Minchev, Z., 2013, “2D vs 3D Visualization and Social Networks Entertainment Games: A Human Factor Response Case Study”, ss. 107–113. In Springer, Berlin, Heidelberg.
32. Mumtaz, W., Xia, L., Malik, A.S., Member, S., Azhar, M., and Yasin, M., 2013, “EEG Classification of Physiological Conditions in 2D / 3D Environments Using Neural Network”, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Sayı 1, ss. 4235–4238.
33. Neto, E., Biessmann, F., Aurlien, H., Nordby, H., and Eichele, T., 2016, Regularized linear discriminant analysis of EEG features in dementia patients: Frontiers in Aging Neuroscience, Sayı 8.
34. Nityananda, V., and Read, J.C.A., 2017, “Stereopsis in animals: evolution, function and mechanisms.”, The Journal of Experimental Biology, Sayı 220, ss. 2502–2512.
35. Oh, S.-H., Lee, Y.-R., and Kim, H.-N., Forthcoming, 2014, “A Novel EEG Feature Extraction Method Using Hjorth Parameter”, Sayı 2, ss. 106–110.
36. Patterson, R., 2007, “Human factors of 3-D displays”, Journal of the Society for Information Display, Sayı 15, ss. 861.
37. Preuß, M., Preiss, S., Syrbe, S., Nestler, U., Fischer, L., Merkenschlager, A., Bertsche, A., Christiansen, H., and Bernhard, M.K., 2015, “Signs and symptoms of pediatric brain tumors and diagnostic value of preoperative EEG”, Child’s Nervous System, Sayı 31, ss. 2051–2054.
38. Ramadan, M.Z., Alhaag, M.H., Abidi, M.H., Ramadan, M.Z., Alhaag, M.H., Abidi, M.H., and Haider Abidi, M., 2017, “Effects of Viewing Displays from Different Distances on Human Visual System”, Applied Sciences, Sayı 7, ss. 1153.
39. Ramos-Aguilar, R., Olvera-López, J.A., and Olmos-Pineda, I., 2017, “Analysis of EEG Signal Processing Techniques Based on Spectrograms”, Research in Computing Science, Sayı 145.
40. Rémi, J., 2009, “The role of EEG in epilepsy: A critical review”, Epilepsy & Behavior, Sayı 15, ss. 22–33. Salai Selvam, V., and Shenbaga Devi, S., 2015, “Analysis of Spectral Features of EEG signal in Brain Tumor Condition”, MEASUREMENT SCIENCE REVIEW, Sayı 15.
41. Smith, S.J.M., 2005, EEG in the diagnosis, “classification, and management of patients with epilepsy”, Journal of Neurology, Neurosurgery, and Psychiatry, Sayı 76 Suppl 2, ss. ii2-7.
42. Subasi, A., 2005, “Automatic recognition of alertness level from EEG by using neural network and wavelet coefficients”, Expert Systems with Applications, Sayı 28, ss. 701–711.
43. Subha, D.P., Joseph, P.K., Acharya U, R., and Lim, C.M., 2010, “EEG Signal Analysis: A Survey”, Journal of Medical Systems, Sayı 34, ss. 195–212.
44. Teplan, 2002, {Fundamentals of EEG measurement}: Measurement Science Review, Sayı 2.
45. Ting, S., Tan, T., West, G., Squelch, A., and Foster, J., 2011, “Quantitative assessment of 2D versus 3D visualisation modalities”, 2011 Visual Communications and Image Processing (VCIP), ss. 1–4.
46. Voiculescu, M., Segarceanu, A., Negutu, M., Ghita, I., Fulga, I., and Oa, C., 2015, “The effect of caffeine on cerebral asymmetry in rats”, Journal of Medicine and Life, Sayı 8, ss. 476–482.
47. Wang, Q., Sourina, O., and Nguyen, M.K., 2010, “EEG-Based "Serious" Games Design for Medical Applications”, 2010 International Conference on Cyberworlds, ss. 270–276.
48. Xilisoft (2019, December 30,). Xilisoft 3D Video Converter - 3D converter, convert to 3D video, (n.d.). http://www.xilisoft.com/3d-video-converter.html
49. Zhu, J., Zhang, E., and Rio‐Tsonis, K. Del, Forthcoming, Eye Anatomy:
50. Zou, B., Liu, Y., Guo, M., and Wang, Y., 2015, “EEG-Based Assessment of Stereoscopic 3D Visual Fatigue Caused by Vergence-Accommodation Conflict”, Journal of Display Technology, Sayı 11, ss. 1076–1083.
51. Zwezdochkina, N., and Antipov, V., 2018, “The EEG Activity during Binocular Depth Perception of 2D Images”, Computational Intelligence and Neuroscience, Sayı 2018, ss. 1–7.
52. 3DN3D, (2019, March 6) 3D Video Chain Saw! - YouTube, n.d. https://www.youtube.com/watch?v=foQNrtUsEjw