Frontal-occipital network alterations while viewing 2D & 3D movies: a source-level EEG and graph theory approach
-
Minchang Yu
,
Shasha Xiao
und
Yingjie Li
Abstract
Many researchers have measured the differences in electroencephalography (EEG) while viewing 2D and 3D movies to uncover the neuromechanism underlying distinct viewing experiences. Using whole-brain network analyses of scalp EEG, our previous study reported that beta and gamma bands presented higher global efficiencies while viewing 3D movies. However, scalp EEG is influenced by volume conduction, not allowing inference from a neuroanatomy perspective; thus, source reconstruction techniques are recommended. This paper is the first to measure the differences in the frontal-occipital networks in EEG source space during 2D and 3D movie viewing. EEG recordings from 40 subjects were performed during 2D and 3D movie viewing. We constructed frontal-occipital networks of alpha, beta, and gamma bands in EEG source space and analyzed network efficiencies. We found that the beta band exhibited higher global efficiency in 3D movie viewing than in 2D movie viewing; however, the alpha global efficiency was not statistically significant. In addition, a support vector machine (SVM) classifier, taking functional connectivities as classification features, was built to identify whether the frontal-occipital networks contain patterns that could distinguish 2D and 3D movie viewing. Using the 6 most important functional connectivity features of the beta band, we obtained the best accuracy of 0.933. Our findings shed light on uncovering the neuromechanism underlying distinct experiences while viewing 2D and 3D movies.
Funding source: National Natural Science Foundation of China
Award Identifier / Grant number: 61571283
Funding source: National Social Science Fund of China
Award Identifier / Grant number: 17BC043
Acknowledgments
We would like to express our gratitude to Minlei Hua and Haibao Li for their assistance in collecting EEG data.
-
Research funding: This work was funded by the National Natural Science Foundation of China (No. 61571283) and the National Social Science Fund of China (No. 17BC043). The funding organizations played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.
-
Author contributions: Minchang Yu: methodology, software, validation, formal analysis, writing – original draft, visualization. Shasha Xiao: writing – review & editing, data curation. Yingjie Li: supervision, investigation, conceptualization, writing – review & editing, resources. Feng Tian: conceptualization, investigation, resources, project administration, funding acquisition.
-
Competing interests: Authors state no conflict of interest.
-
Informed consent: Informed consent was obtained from all individuals included in this study.
-
Ethical approval: The experiment was approved by the Shanghai Ethics Committee for Clinical Research (approval number: SECCR/2018-15-01) and followed the tenets of the Declaration of Helsinki.
References
1. Amin, HU, Ousta, F, Yusoff, MZ, Malik, AS. Modulation of cortical activity in response to learning and long-term memory retrieval of 2D verses stereoscopic 3D educational contents: evidence from an EEG study. Comput Hum Behav 2021;114:106526. https://doi.org/10.1016/j.chb.2020.106526.Suche in Google Scholar
2. Lee, EC, Heo, H, Park, KR. The comparative measurements of eyestrain caused by 2D and 3D displays. IEEE Trans Consum Electron 2010;56:1677–83. https://doi.org/10.1109/tce.2010.5606312.Suche in Google Scholar
3. Chen, C, Li, J, Lu, X. Multiscale entropy-based analysis and processing of EEG signal during watching 3DTV. Measurement 2018;125:432–7. https://doi.org/10.1016/j.measurement.2018.05.017.Suche in Google Scholar
4. Tian, F, Zhang, Y, Li, H, Hua, M, Li, Y. Comparison of VR movies and traditional 2D movies: a study on brain waves. In: 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). Vol 1. IEEE; 2020. p. 2507–11. https://doi.org/10.1109/itnec48623.2020.9084744.Suche in Google Scholar
5. Sporns, O. The human connectome: a complex network. Ann N Y Acad Sci 2011;1224:109–25. https://doi.org/10.1111/j.1749-6632.2010.05888.x.Suche in Google Scholar PubMed
6. Yu, M, Li, Y, Tian, F. Responses of functional brain networks while watching 2D and 3D videos: an EEG study. Biomed Signal Process Control 2021;68:102613. https://doi.org/10.1016/j.bspc.2021.102613.Suche in Google Scholar
7. Lai, M, Demuru, M, Hillebrand, A, Fraschini, M. A comparison between scalp-and source-reconstructed EEG networks. Sci Rep 2018;8:1–8. https://doi.org/10.1038/s41598-018-30869-w.Suche in Google Scholar PubMed PubMed Central
8. Brunner, C, Billinger, M, Seeber, M, Mullen, TR, Makeig, S. Volume conduction influences scalp-based connectivity estimates. Front Comput Neurosci 2016;10:121. https://doi.org/10.3389/fncom.2016.00121.Suche in Google Scholar PubMed PubMed Central
9. Sehatpour, P, Molholm, S, Schwartz, TH, Mahoney, JR, Mehta, AD, Javitt, DC, et al.. A human intracranial study of long-range oscillatory coherence across a frontal–occipital–hippocampal brain network during visual object processing. Proc Natl Acad Sci USA 2008;105:4399–404. https://doi.org/10.1073/pnas.0708418105.Suche in Google Scholar PubMed PubMed Central
10. Duan, F, Watanabe, K, Yoshimura, Y, Kikuchi, M, Minabe, Y, Aihara, K. Relationship between brain network pattern and cognitive performance of children revealed by MEG signals during free viewing of video. Brain Cognit 2014;86:10–6. https://doi.org/10.1016/j.bandc.2014.01.011.Suche in Google Scholar PubMed
11. Calore, E, Folgieri, R, Gadia, D, Marini, D. Analysis of brain activity and response during monoscopic and stereoscopic visualization. In: Stereoscopic displays and applications XXIII. Vol 8288. International Society for Optics and Photonics; 2012:82880M. https://doi.org/10.1117/12.906708.Suche in Google Scholar
12. Delorme, A, Makeig, S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods 2004;134:9–21. https://doi.org/10.1016/j.jneumeth.2003.10.009.Suche in Google Scholar PubMed
13. Gramfort, A, Luessi, M, Larson, E, Engemann, DA, Strohmeier, D, Brodbeck, C, et al.. MEG and EEG data analysis with MNE-Python. Front Neurosci 2013;7:267. https://doi.org/10.3389/fnins.2013.00267.Suche in Google Scholar PubMed PubMed Central
14. Desikan, RS, Ségonne, F, Fischl, B, Quinn, BT, Dickerson, BC, Blacker, D, et al.. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 2006;31:968–80. https://doi.org/10.1016/j.neuroimage.2006.01.021.Suche in Google Scholar PubMed
15. Kitzbichler, MG, Henson, RNA, Smith, ML, Nathan, PJ, Bullmore, ET. Cognitive effort drives workspace configuration of human brain functional networks. J Neurosci 2011;31:8259–70. https://doi.org/10.1523/jneurosci.0440-11.2011.Suche in Google Scholar
16. Watson, AW, Okello, EJ, Brooker, HJ, Lester, S, McDougall, GJ, Wesnes, KA. The impact of blackcurrant juice on attention, mood and brain wave spectral activity in young healthy volunteers. Nutr Neurosci 2019;22:596–606. https://doi.org/10.1080/1028415x.2017.1420539.Suche in Google Scholar PubMed
17. Yu, H, Li, X, Lei, X, Wang, J. Modulation effect of acupuncture on functional brain networks and classification of its manipulation with EEG signals. IEEE Trans Neural Syst Rehabil Eng 2019;27:1973–84. https://doi.org/10.1109/tnsre.2019.2939655.Suche in Google Scholar PubMed
18. Lowet, E, Roberts, MJ, Bonizzi, P, Karel, J, De Weerd, P. Quantifying neural oscillatory synchronization: a comparison between spectral coherence and phase-locking value approaches. PLoS One 2016;11:e0146443. https://doi.org/10.1371/journal.pone.0146443.Suche in Google Scholar PubMed PubMed Central
19. Bullmore, E, Sporns, O. The economy of brain network organization. Nat Rev Neurosci 2012;13:336–49. https://doi.org/10.1038/nrn3214.Suche in Google Scholar PubMed
20. Stanley, ML, Simpson, SL, Dagenbach, D, Lyday, RG, Burdette, JH, Laurienti, PJ. Changes in brain network efficiency and working memory performance in aging. PLoS One 2015;10:e0123950. https://doi.org/10.1371/journal.pone.0123950.Suche in Google Scholar PubMed PubMed Central
21. Yuvaraj, R, Murugappan, M, Ibrahim, NM, Sundaraj, K, Omar, MI, Mohamad, K, et al.. Optimal set of EEG features for emotional state classification and trajectory visualization in Parkinson’s disease. Int J Psychophysiol 2014;94:482–95. https://doi.org/10.1016/j.ijpsycho.2014.07.014.Suche in Google Scholar PubMed
22. Pedregosa, F, Varoquaux, G, Gramfort, A, Michel, V, Thirion, B, Grisel, O, et al.. Scikit-learn: machine learning in Python. J Mach Learn Res 2011;12:2825–30.Suche in Google Scholar
23. Wang, L, Huang, C, Yang, JY. Predicting siRNA potency with random forests and support vector machines. BMC Genom 2010;11:1–7. https://doi.org/10.1186/1471-2164-11-S3-S2.Suche in Google Scholar PubMed PubMed Central
24. Larsson, J, Heeger, DJ. Two retinotopic visual areas in human lateral occipital cortex. J Neurosci 2006;26:13128–42. https://doi.org/10.1523/jneurosci.1657-06.2006.Suche in Google Scholar
25. Alexander, WH, Brown, JW. Frontal cortex function as derived from hierarchical predictive coding. Sci Rep 2018;8:1–11. https://doi.org/10.1038/s41598-018-21407-9.Suche in Google Scholar PubMed PubMed Central
26. Parks, EL, Madden, DJ. Brain connectivity and visual attention. Brain Connect 2013;3:317–38. https://doi.org/10.1089/brain.2012.0139.Suche in Google Scholar PubMed PubMed Central
27. Sjoerds, Z, Stufflebeam, SM, Veltman, DJ, Van den Brink, W, Penninx, BWJH, Douw, L. Loss of brain graph network efficiency in alcohol dependence. Addiction Biol 2017;22:523–34. https://doi.org/10.1111/adb.12346.Suche in Google Scholar PubMed PubMed Central
28. Abbas, AK, Azemi, G, Amiri, S, Ravanshadi, S, Omidvarnia, A. Effective connectivity in brain networks estimated using EEG signals are altered in children with attention deficit hyperactivity disorder. Comput Biol Med 2021:104515. https://doi.org/10.1016/j.compbiomed.2021.104515.Suche in Google Scholar PubMed
29. Gerjets, P, Scheiter, K, Cierniak, G. The scientific value of cognitive load theory: a research agenda based on the structuralist view of theories. Educ Psychol Rev 2009;21:43–54. https://doi.org/10.1007/s10648-008-9096-1.Suche in Google Scholar
30. Malik, AS, Khairuddin, RNHR, Amin, HU, Smith, ML, Kamel, N, Abdullah, JM, et al.. EEG based evaluation of stereoscopic 3D displays for viewer discomfort. Biomed Eng Online 2015;14:1–21. https://doi.org/10.1186/s12938-015-0006-8.Suche in Google Scholar PubMed PubMed Central
31. Van Der Land, S, Schouten, AP, Feldberg, F, Van Den Hooff, B, Huysman, M. Lost in space? Cognitive fit and cognitive load in 3D virtual environments. Comput Hum Behav 2013;29:1054–64. https://doi.org/10.1016/j.chb.2012.09.006.Suche in Google Scholar
32. Biercewicz, K, Borawski, M, Duda, J. Method for selecting an engagement index for a specific type of game using cognitive neuroscience. Int J Comput Games Technol 2020;2020:2450651. https://doi.org/10.1155/2020/2450651.Suche in Google Scholar
33. Biau, G, Scornet, E. A random forest guided tour. Test 2016;25:197–227. https://doi.org/10.1007/s11749-016-0481-7.Suche in Google Scholar
34. Li, J, Chen, C, Liu, Y, Chen, X. Small-world brain functional network altered by watching 2D/3DTV. J Vis Commun Image Represent 2016;38:433–9. https://doi.org/10.1016/j.jvcir.2016.03.023.Suche in Google Scholar
© 2022 Walter de Gruyter GmbH, Berlin/Boston
Artikel in diesem Heft
- Frontmatter
- Research Articles
- Towards fully automated detection of epileptic disorders: a novel CNSVM approach with Clough–Tocher interpolation
- Frontal-occipital network alterations while viewing 2D & 3D movies: a source-level EEG and graph theory approach
- Real-time recognition of different imagined actions on the same side of a single limb based on the fNIRS correlation coefficient
- Simultaneous validation of wearable motion capture system for lower body applications: over single plane range of motion (ROM) and gait activities
- Embedded system design for classification of COPD and pneumonia patients by lung sound analysis
- Investigation of the impact of electromagnetic fields emitted close to the head by smart glasses
- A method to classify bone marrow cells with rejected option
Artikel in diesem Heft
- Frontmatter
- Research Articles
- Towards fully automated detection of epileptic disorders: a novel CNSVM approach with Clough–Tocher interpolation
- Frontal-occipital network alterations while viewing 2D & 3D movies: a source-level EEG and graph theory approach
- Real-time recognition of different imagined actions on the same side of a single limb based on the fNIRS correlation coefficient
- Simultaneous validation of wearable motion capture system for lower body applications: over single plane range of motion (ROM) and gait activities
- Embedded system design for classification of COPD and pneumonia patients by lung sound analysis
- Investigation of the impact of electromagnetic fields emitted close to the head by smart glasses
- A method to classify bone marrow cells with rejected option
