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Year 2020, Volume: 3 Issue: 2, 131 - 148, 28.08.2020

Negin Manshourı , Mesut Melek , Temel Kayıkçıoğlu

https://doi.org/10.35377/saucis.03.02.754771

Abstract

References

  • R. N. H. R. Khairuddin, A. S. Malik, and N. Kamel, “EEG Topographical Maps Analysis for 2D and 3D Video Game Play,” 2014 5th International Conference on Intelligent and Advanced Systems (ICIAS), no. June, pp. 1–4, 2014.
  • H.-G. Jeong et al., “The impact of 3D and 2D TV watching on neurophysiological responses and cognitive functioning in adults,” The European Journal of Public Health, vol. 25, no. 6, pp. 1047–1052, Dec. 2015.
  • Q. Wang, O. Sourina, and M. K. Nguyen, “EEG-Based "Serious" Games Design for Medical Applications,” in 2010 International Conference on Cyberworlds, 2010, pp. 270–276.
  • P. Benzie et al., “A Survey of 3DTV Displays: Techniques and Technologies,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 17, no. 11, pp. 1647–1658, Nov. 2007.
  • M. S. Banks, J. C. A. Read, R. S. Allison, and S. J. Watt, “Stereoscopy and the Human Visual System,” SMPTE Motion Imaging Journal, vol. 121, no. 4, pp. 24–43, May 2012.
  • D. Bavelier, C. S. Green, D. H. Han, P. F. Renshaw, M. M. Merzenich, and D. A. Gentile, “Brains on video games,” Nature Reviews Neuroscience, vol. 12, no. 12, pp. 763–768, Dec. 2011.
  • M. Gentzkow, “Television and Voter Turnout*,” Quarterly Journal of Economics, vol. 121, no. 3, pp. 931–972, Aug. 2006.
  • N. Manshouri, M. Maleki, and T. Kayikcioglu, “An EEG-based stereoscopic research of the PSD differences in pre and post 2D&3D movies watching,” Biomedical Signal Processing and Control, vol. 55, Jan. 2020.
  • N. Zwezdochkina and V. Antipov, “The EEG Activity during Binocular Depth Perception of 2D Images,” Computational intelligence and neuroscience, vol. 2018, pp. 1–7, 2018.
  • N. Manshouri, M. Maleki, and T. Kayıkçıoğlu, “Classification of Human Vision Discrepancy during Watching 2D and 3D Movies Based on EEG Signals,” International Journal of Computer Science and Information Security, vol. 15, no. 2, pp. 430–436, 2017.
  • D. De Waard, “The Measurement of Drivers’ Mental Workload,” 1996.
  • B. J. Fisch and R. Spehlmann, Fisch and Spehlmann’s EEG primer : basic principles of digital and analog EEG. Elsevier, 1999.
  • V. Nityananda and J. C. A. Read, “Stereopsis in animals: evolution, function and mechanisms.,” The Journal of experimental biology, vol. 220, no. Pt 14, pp. 2502–2512, 2017.
  • J. C. A. Read, “What is stereoscopic vision good for?,” 2015.
  • M.-M. Hamed, E. Marzieh, and Ag. David, “The relationship between binocular vision symptoms and near point of convergence,” Indian Journal of Ophthalmology, vol. 61, no. 7, p. 325, Jul. 2013.
  • C. Wheatstone, “On some remarkable and hitherto unobserved phenomena of binocular vision.,” The Optometric weekly, vol. 53, pp. 2311–5, Nov. 1962.
  • M. Z. Ramadan et al., “Effects of Viewing Displays from Different Distances on Human Visual System,” Applied Sciences, vol. 7, no. 11, p. 1153, Nov. 2017.
  • H. R. Khairuddin et al., “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), pp. 2064–2067, 2013.
  • Z. Minchev, “2D vs 3D Visualization and Social Networks Entertainment Games: A Human Factor Response Case Study,” Springer, Berlin, Heidelberg, 2013, pp. 107–113.
  • N. Manshouri and T. Kayikcioglu, “A Comprehensive Analysis of 2D&3D Video Watching of EEG Signals by Increasing PLSR and SVM Classification Results,” The Computer Journal, May 2019.
  • W. Avarvand, Forooz Shahbazi and Bosse, Sebastian and Muller, Klaus-Robert and Schufer, Ralf and Nolte, Guido and Wiegand, Thomas and Curio, Gabriel and Samek, “Objective quality assessment of stereoscopic images with vertical disparity using EEG,” Journal of neural engineering, vol. 14, no. 4, pp. 1–14, 2017.
  • S. Ting, T. Tan, G. West, A. Squelch, and J. Foster, “Quantitative assessment of 2D versus 3D visualisation modalities,” in 2011 Visual Communications and Image Processing (VCIP), 2011, pp. 1–4.
  • A. Carvajal, “Quantitative Comparison between the Use of 3D vs 2D Visualization Tools to Present Building Design Proposals to Non-Spatial Skilled End Users,” in Ninth International Conference on Information Visualisation (IV’05), pp. 291–294.
  • S. E. Kober, J. Kurzmann, and C. Neuper, “Cortical correlate of spatial presence in 2D and 3D interactive virtual reality: An EEG study,” International Journal of Psychophysiology, vol. 83, no. 3, pp. 365–374, Mar. 2012.
  • Y. Han, H. Y. Lin, and C. Chen, “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, pp. 9–12, 2017.
  • F. P. S. Fischmeister and H. Bauer, “Neural correlates of monocular and binocular depth cues based on natural images : A LORETA analysis,” Vision research, vol. 46, no. 20, pp. 3373–3380, 2006.
  • (1) 3D Video Chain Saw! - YouTube. .
  • “Xilisoft 3D Video Converter - 3D converter, convert to 3D video.” [Online]. Available: http://www.xilisoft.com/3d-video-converter.html. [Accessed: 30-Dec-2019].
  • “Download Free 3D Video Converter - Convert 2D to 3D | IQmango Free Software.” [Online]. Available: http://iqmango.com/3DVideo_Converter.html. [Accessed: 30-Dec-2019].
  • “Easiest Video Editing Software Free Download.” [Online]. Available: http://www.idooeditor.com/. [Accessed: 30-Dec-2019].
  • J. Kim et al., “A full-color anaglyph three-dimensional display system using active color filter glasses,” Journal of Information Display, vol. 12, no. 1, pp. 37–41, Mar. 2011.
  • K. Eroğlu, T. Kayıkçıoğlu, and O. Osman, “Effect of brightness of visual stimuli on EEG signals,” Behavioural Brain Research, vol. 382, p. 112486, Mar. 2020.
  • N. Roehri, J. M. Lina, J. C. Mosher, F. Bartolomei, and C. G. Benar, “Time-Frequency Strategies for Increasing High-Frequency Oscillation Detectability in Intracerebral EEG,” IEEE Transactions on Biomedical Engineering, vol. 63, no. 12, pp. 2595–2606, Dec. 2016.
  • N. Kehtarnavaz, “Frequency Domain Processing,” in Digital Signal Processing System Design, Elsevier, 2008, pp. 175–196.
  • F. Hlawatsch and G. F. Boudreaux-Bartels, “Linear and quadratic time-frequency signal representations,” IEEE Signal Processing Magazine, vol. 9, no. 2, pp. 21–67, Apr. 1992.
  • S.-H. Oh, Y.-R. Lee, and H.-N. Kim, “A Novel EEG Feature Extraction Method Using Hjorth Parameter.”
  • T. Kayikcioglu, M. Maleki, and K. Eroglu, “Fast and accurate PLS-based classification of EEG sleep using single channel data,” Expert Systems with Applications, vol. 42, no. 21, pp. 7825–7830, Jun. 2015.
  • E. Fix and J. L. Hodges, “Discriminatory Analysis. Nonparametric Discrimination: Consistency Properties,” International Statistical Review / Revue Internationale de Statistique, vol. 57, no. 3, p. 238, Dec. 1989.
  • N. S. Altman, “An introduction to kernel and nearest-neighbor nonparametric regression,” American Statistician, vol. 46, no. 3, pp. 175–185, 1992.
  • A. M. Martinez and A. C. Kak, “PCA versus LDA,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 2, pp. 228–233, Feb. 2001.
  • M. Hosťovecký and B. B, “Brain activity: beta wave analysis of 2D and 3D serious games using EEG,” JAMSI, vol. 13, no. 2, 2017.
  • C. Chen, K. Li, Q. Wu, H. Wang, Z. Qian, and G. Sudlow, “EEG-based detection and evaluation of fatigue caused by watching 3DTV,” Displays, vol. 34, no. 2, pp. 81–88, Apr. 2013.
  • C. Chen et al., “Assessment visual fatigue of watching 3DTV using EEG power spectral parameters,” Displays, vol. 35, no. 5, pp. 266–272, Dec. 2014.
  • A. S. Malik et al., “EEG based evaluation of stereoscopic 3D displays for viewer discomfort,” BioMedical Engineering OnLine, vol. 14, no. 1, p. 21, Dec. 2015.
  • S. Kim and D. Kim, “Differences in the Brain Waves of 3D and 2 . 5D Motion Picture Viewers,” arXiv preprint arXiv:1210.2147.

The Efficacy of Frontal and Temporal Lobes in Detecting 2D&3D Video Transition Using EEG Power

Year 2020, Volume: 3 Issue: 2, 131 - 148, 28.08.2020

Negin Manshourı , Mesut Melek , Temel Kayıkçıoğlu

https://doi.org/10.35377/saucis.03.02.754771

Abstract

Due to the three-dimensional (3D) structure of the human eye, 3D technology was used in this research. Transition to 2D and 3D analysis is important, claiming that binocular vision will lose dimension during fatigue. Thus, a single-stream video consisting of random 2D&3D parts was watched by nine volunteers. The dynamic behavior and power spectral density (PSD) of the volunteers’ brain signals were considered for a comprehensive quantitative analysis. The dominant EEG bands and time intervals were selected in 2D to 3D (2D_3D) and 3D to 2D (3D_2D) transitions by analyzing power differences based on short-time Fourier transformation (STFT). Taking into account this information, applying effective feature extraction and classification techniques, the behavioral patterns of channels representing the brain lobes of the volunteers were analyzed. Hjorth parameters and statistical methods were used as feature extraction methods. The k-nearest neighbors (k-NN) and linear discriminant analysis (LDA) algorithms were applied to classify 2D_3D and 3D_2D transitions. The results revealed that, thanks to the comprehensive classification analysis of the 2D_3D and 3D_2D transitions, the change in the activity power of the brain cortex can be represented. The dominance of the temporal and frontal lobes between the channels representing these transitions cannot be excluded.

Keywords

EEG 2D&3D transition

References

  • R. N. H. R. Khairuddin, A. S. Malik, and N. Kamel, “EEG Topographical Maps Analysis for 2D and 3D Video Game Play,” 2014 5th International Conference on Intelligent and Advanced Systems (ICIAS), no. June, pp. 1–4, 2014.
  • H.-G. Jeong et al., “The impact of 3D and 2D TV watching on neurophysiological responses and cognitive functioning in adults,” The European Journal of Public Health, vol. 25, no. 6, pp. 1047–1052, Dec. 2015.
  • Q. Wang, O. Sourina, and M. K. Nguyen, “EEG-Based "Serious" Games Design for Medical Applications,” in 2010 International Conference on Cyberworlds, 2010, pp. 270–276.
  • P. Benzie et al., “A Survey of 3DTV Displays: Techniques and Technologies,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 17, no. 11, pp. 1647–1658, Nov. 2007.
  • M. S. Banks, J. C. A. Read, R. S. Allison, and S. J. Watt, “Stereoscopy and the Human Visual System,” SMPTE Motion Imaging Journal, vol. 121, no. 4, pp. 24–43, May 2012.
  • D. Bavelier, C. S. Green, D. H. Han, P. F. Renshaw, M. M. Merzenich, and D. A. Gentile, “Brains on video games,” Nature Reviews Neuroscience, vol. 12, no. 12, pp. 763–768, Dec. 2011.
  • M. Gentzkow, “Television and Voter Turnout*,” Quarterly Journal of Economics, vol. 121, no. 3, pp. 931–972, Aug. 2006.
  • N. Manshouri, M. Maleki, and T. Kayikcioglu, “An EEG-based stereoscopic research of the PSD differences in pre and post 2D&3D movies watching,” Biomedical Signal Processing and Control, vol. 55, Jan. 2020.
  • N. Zwezdochkina and V. Antipov, “The EEG Activity during Binocular Depth Perception of 2D Images,” Computational intelligence and neuroscience, vol. 2018, pp. 1–7, 2018.
  • N. Manshouri, M. Maleki, and T. Kayıkçıoğlu, “Classification of Human Vision Discrepancy during Watching 2D and 3D Movies Based on EEG Signals,” International Journal of Computer Science and Information Security, vol. 15, no. 2, pp. 430–436, 2017.
  • D. De Waard, “The Measurement of Drivers’ Mental Workload,” 1996.
  • B. J. Fisch and R. Spehlmann, Fisch and Spehlmann’s EEG primer : basic principles of digital and analog EEG. Elsevier, 1999.
  • V. Nityananda and J. C. A. Read, “Stereopsis in animals: evolution, function and mechanisms.,” The Journal of experimental biology, vol. 220, no. Pt 14, pp. 2502–2512, 2017.
  • J. C. A. Read, “What is stereoscopic vision good for?,” 2015.
  • M.-M. Hamed, E. Marzieh, and Ag. David, “The relationship between binocular vision symptoms and near point of convergence,” Indian Journal of Ophthalmology, vol. 61, no. 7, p. 325, Jul. 2013.
  • C. Wheatstone, “On some remarkable and hitherto unobserved phenomena of binocular vision.,” The Optometric weekly, vol. 53, pp. 2311–5, Nov. 1962.
  • M. Z. Ramadan et al., “Effects of Viewing Displays from Different Distances on Human Visual System,” Applied Sciences, vol. 7, no. 11, p. 1153, Nov. 2017.
  • H. R. Khairuddin et al., “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), pp. 2064–2067, 2013.
  • Z. Minchev, “2D vs 3D Visualization and Social Networks Entertainment Games: A Human Factor Response Case Study,” Springer, Berlin, Heidelberg, 2013, pp. 107–113.
  • N. Manshouri and T. Kayikcioglu, “A Comprehensive Analysis of 2D&3D Video Watching of EEG Signals by Increasing PLSR and SVM Classification Results,” The Computer Journal, May 2019.
  • W. Avarvand, Forooz Shahbazi and Bosse, Sebastian and Muller, Klaus-Robert and Schufer, Ralf and Nolte, Guido and Wiegand, Thomas and Curio, Gabriel and Samek, “Objective quality assessment of stereoscopic images with vertical disparity using EEG,” Journal of neural engineering, vol. 14, no. 4, pp. 1–14, 2017.
  • S. Ting, T. Tan, G. West, A. Squelch, and J. Foster, “Quantitative assessment of 2D versus 3D visualisation modalities,” in 2011 Visual Communications and Image Processing (VCIP), 2011, pp. 1–4.
  • A. Carvajal, “Quantitative Comparison between the Use of 3D vs 2D Visualization Tools to Present Building Design Proposals to Non-Spatial Skilled End Users,” in Ninth International Conference on Information Visualisation (IV’05), pp. 291–294.
  • S. E. Kober, J. Kurzmann, and C. Neuper, “Cortical correlate of spatial presence in 2D and 3D interactive virtual reality: An EEG study,” International Journal of Psychophysiology, vol. 83, no. 3, pp. 365–374, Mar. 2012.
  • Y. Han, H. Y. Lin, and C. Chen, “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, pp. 9–12, 2017.
  • F. P. S. Fischmeister and H. Bauer, “Neural correlates of monocular and binocular depth cues based on natural images : A LORETA analysis,” Vision research, vol. 46, no. 20, pp. 3373–3380, 2006.
  • (1) 3D Video Chain Saw! - YouTube. .
  • “Xilisoft 3D Video Converter - 3D converter, convert to 3D video.” [Online]. Available: http://www.xilisoft.com/3d-video-converter.html. [Accessed: 30-Dec-2019].
  • “Download Free 3D Video Converter - Convert 2D to 3D | IQmango Free Software.” [Online]. Available: http://iqmango.com/3DVideo_Converter.html. [Accessed: 30-Dec-2019].
  • “Easiest Video Editing Software Free Download.” [Online]. Available: http://www.idooeditor.com/. [Accessed: 30-Dec-2019].
  • J. Kim et al., “A full-color anaglyph three-dimensional display system using active color filter glasses,” Journal of Information Display, vol. 12, no. 1, pp. 37–41, Mar. 2011.
  • K. Eroğlu, T. Kayıkçıoğlu, and O. Osman, “Effect of brightness of visual stimuli on EEG signals,” Behavioural Brain Research, vol. 382, p. 112486, Mar. 2020.
  • N. Roehri, J. M. Lina, J. C. Mosher, F. Bartolomei, and C. G. Benar, “Time-Frequency Strategies for Increasing High-Frequency Oscillation Detectability in Intracerebral EEG,” IEEE Transactions on Biomedical Engineering, vol. 63, no. 12, pp. 2595–2606, Dec. 2016.
  • N. Kehtarnavaz, “Frequency Domain Processing,” in Digital Signal Processing System Design, Elsevier, 2008, pp. 175–196.
  • F. Hlawatsch and G. F. Boudreaux-Bartels, “Linear and quadratic time-frequency signal representations,” IEEE Signal Processing Magazine, vol. 9, no. 2, pp. 21–67, Apr. 1992.
  • S.-H. Oh, Y.-R. Lee, and H.-N. Kim, “A Novel EEG Feature Extraction Method Using Hjorth Parameter.”
  • T. Kayikcioglu, M. Maleki, and K. Eroglu, “Fast and accurate PLS-based classification of EEG sleep using single channel data,” Expert Systems with Applications, vol. 42, no. 21, pp. 7825–7830, Jun. 2015.
  • E. Fix and J. L. Hodges, “Discriminatory Analysis. Nonparametric Discrimination: Consistency Properties,” International Statistical Review / Revue Internationale de Statistique, vol. 57, no. 3, p. 238, Dec. 1989.
  • N. S. Altman, “An introduction to kernel and nearest-neighbor nonparametric regression,” American Statistician, vol. 46, no. 3, pp. 175–185, 1992.
  • A. M. Martinez and A. C. Kak, “PCA versus LDA,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 2, pp. 228–233, Feb. 2001.
  • M. Hosťovecký and B. B, “Brain activity: beta wave analysis of 2D and 3D serious games using EEG,” JAMSI, vol. 13, no. 2, 2017.
  • C. Chen, K. Li, Q. Wu, H. Wang, Z. Qian, and G. Sudlow, “EEG-based detection and evaluation of fatigue caused by watching 3DTV,” Displays, vol. 34, no. 2, pp. 81–88, Apr. 2013.
  • C. Chen et al., “Assessment visual fatigue of watching 3DTV using EEG power spectral parameters,” Displays, vol. 35, no. 5, pp. 266–272, Dec. 2014.
  • A. S. Malik et al., “EEG based evaluation of stereoscopic 3D displays for viewer discomfort,” BioMedical Engineering OnLine, vol. 14, no. 1, p. 21, Dec. 2015.
  • S. Kim and D. Kim, “Differences in the Brain Waves of 3D and 2 . 5D Motion Picture Viewers,” arXiv preprint arXiv:1210.2147.
There are 45 citations in total.

Details

Primary Language English
Subjects Software Engineering (Other), Electrical Engineering
Journal Section Articles
Authors

Negin Manshourı 0000-0001-5297-5545

Mesut Melek 0000-0002-7152-7788

Temel Kayıkçıoğlu 0000-0002-6787-2415

Publication Date August 28, 2020
Submission Date June 18, 2020
Acceptance Date July 28, 2020
Published in Issue Year 2020Volume: 3 Issue: 2

Cite

IEEE N. Manshourı, M. Melek, and T. Kayıkçıoğlu, “The Efficacy of Frontal and Temporal Lobes in Detecting 2D&3D Video Transition Using EEG Power”, SAUCIS, vol. 3, no. 2, pp. 131–148, 2020, doi: 10.35377/saucis.03.02.754771.
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