Abstract
Networks play an important role in studying structure or functional connection of various brain areas, and explaining mechanism of emotion. However, there is a lack of comprehensive analysis among different construction methods nowadays. Therefore, this paper studies the impact of different emotions on connection of functional brain networks (FBNs) based on electroencephalogram (EEG). Firstly, we defined electrode node as brain area of vicinity of electrode to construct 32-node small-scale FBN. Pearson correlation coefficient (PCC) was used to construct correlation-based FBNs. Phase locking value (PLV) and phase synchronization index (PSI) were utilized to construct synchrony-based FBNs. Next, global properties and effects of emotion of different networks were compared. The difference of synchrony-based FBN concentrates in alpha band, and the number of differences is less than that of correlation-based FBN. Node properties of different small-scale FBNs have significant differences, offering a new basis for feature extraction of recognition regions in emotional FBNs. Later, we made partition of electrode nodes and 10 new brain areas were defined as regional nodes to construct 10-node large-scale FBN. Results show the impact of emotion on network clusters on the right forehead, and high valence enhances information processing efficiency of FBN by promoting connections in brain areas.
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Data availability
The data used to support the findings of this study are database for emotion analysis using physical signals (DEAP): http://www.eecs.qmul.ac.uk/mmv/datasets/deap/readme.html.
References
Padhmashree V, Bhattacharyya A (2022) Human emotion recognition based on time-frequency analysis of multivariate EEG signal. Knowledge-Based Syst 238:107867. https://doi.org/10.1016/j.knosys.2021.107867
Liang Z, Oba S, Ishii S (2019) An unsupervised EEG decoding system for human emotion recognition. Neural Netw 116:257–268. https://doi.org/10.1016/j.neunet.2019.04.003
Liu Y, Fu G (2021) Emotion recognition by deeply learned multi-channel textual and EEG features. Futur Gener Comp Syst 119:1–6. https://doi.org/10.1016/j.future.2021.01.010
Chen R, Tang D, Hu L (2015) Measuring of pain based on neurophysiology. J Psychol Sci 38(5):1256–1263. https://doi.org/10.16719/j.cnki.1671-6981.2015.05.030
Goshvarpour A, Goshvarpour A (2021) Innovative Poincare’s plot asymmetry descriptors for EEG emotion recognition. Cogn Neurodyn. https://doi.org/10.1007/s11571-021-09735-5
Cao R (2014) Nonlinear and complex network theory in the application of EEG data analysis research. PhD Dissertation Taiyuan University of Technology, Taiyuan. https://kns.cnki.net/kcms/detail/detail.aspx?dbcode=CDFD&dbname=CDFDLAST2015&filename=1015607448.nh&v=ptbsds12GpT%25mmd2FdGToipWW140UE%25mmd2FhLYKmfp3v%25mmd2B4j2Q5wz2d5UxtDmpVa5FTlep%25mmd2Fd7N. Accessed 16th, July, 2015-15th, August, 2015
Fabrizio FDV, Babiloni F (2010) The graph theoretical approach in brain functional networks theory and applications. https://doi.org/10.2200/S00279ED1V01Y201004BME036
Chen J, Hu B, Wang Y et al (2016) A three-stage decision framework for multi-subject emotion recognition using physiological signals. In: Proceedings of the 2016 IEEE International Conference on Bioinformatics and Biomedicine, pp. 470–474, Shenzhen, China. https://doi.org/10.1109/BIBM.2016.7822562
Sporns O, Tononi G, Kötter R (2005) The human connectome: a structural description of the human brain. Plos Comput Biol 1(4):e42. https://doi.org/10.1371/journal.pcbi.0010042
Lai Y, Gao T, Wu D, Yao D (2008) Research on electroencephalogram of musical emotion perception. J Univ Elect Sci Technol China 37(2): 301–304. https://kns.cnki.net/kcms/detail/detail.aspx?dbcode=CJFD&dbname=CJFD2008&filename=DKDX200802041&v=qhPutXheFFbhFovZiAt8o7RxHwzf8w3dZ7q3%25mmd2FAXfXWgW0cIHhYdt%25mmd2B3%25mmd2FO0waSWUOr. Accessed March 2008
Elam JS, Essen DV (2013) Human connectome project. Encyclopedia Comput Neurosci. https://doi.org/10.1007/978-1-4614-7320-6_592-1
Alivisatos AP, Andrews AM, Boyden ES et al (2013) Nanotools for neuroscience and brain activity mapping. ACS Nano 7(3):1850–1866. https://doi.org/10.1021/nn4012847
Seo EH, Lee DY, Lee JM et al (2013) Whole-brain functional networks in cognitively normal, mild cognitive impairment, and Alzheimer’s disease. PLoS ONE 8(1):e53922. https://doi.org/10.1371/journal.pone.0053922
Garrison KA, Scheinost D, Finn ES, Shen X, Constable RT (2015) The (in)stability of functional brain network measures across thresholds. Neuroimage 118:651–661. https://doi.org/10.1016/j.neuroimage.2015.05.046
Makhtar SN, Senik MH, Stevenson CW, Mason R, Halliday DM (2020) Improved functional connectivity network estimation for brain networks using multivariate partial coherence. J Neural Eng 17:026013. https://doi.org/10.1088/1741-2552/ab7a50
Sengupta A, Routray A, Datta S (2016) Brain networks using nonlinear interdependence-based EEG synchronization: a study of human fatigue. In: Proceedings of 2016 International Conference on Systems in Medicine and Biology, pp. 170–173, IIT Kharagpur, India. https://doi.org/10.1007/978-3-319-56782-2_9069-1
Kirwan B, Bodily T (2017) Graph theory. Encyclopedia Clin Neuropsychol. https://doi.org/10.1007/978-3-319-56782-2_9069-1
Garretón M, Hylandf K, Parra D (2017) Understanding people’s interaction with neural Sci-Art. In: Proceedings of 2017 IEEE VIS Arts Program (VISAP) pp. 1–7, Phoenix, AZ, USA. https://doi.org/10.1109/VISAP.2017.8282366
Rosário RS, Cardoso PT, Muñoz MA, Montoya P, Miranda JGV (2015) Motif-synchronization: a new method for analysis of dynamic brain networks with EEG. Physica A 439:7–19. https://doi.org/10.1016/j.physa.2015.07.018
Wang Q et al (2021) Using convolutional neural networks to decode EEG-based functional brain network with different severity of acrophobia. J Neural Eng 18:016007. https://doi.org/10.1088/1741-2552/abcdbd
Thilaga M, Ramasamy V, Nadarajan R, Nandagopal D (2018) Shortest path based network analysis to characterize cognitive load states of human brain using EEG based functional brain networks. J Integr Neurosci 17(2):133–148. https://doi.org/10.31083/JIN-170049
Tewarie P, Schoonheim MM, Schouten DI et al (2015) Functional brain networks: linking thalamic atrophy to clinical disability in multiple sclerosis, a multimodal fMRI and MEG study. Hum Brain Mapp 36(2):603–618. https://doi.org/10.1002/2Fhbm.22650
Straaten ECW, Stam CJ (2013) Structure out of chaos: functional brain network analysis with EEG, MEG, and functional MRI. Eur Neuropsychopharmacol 23(1):7–18. https://doi.org/10.1016/j.euroneuro.2012.10.010
Xing M, Tadayonnejad R, MacNamara A (2016) EEG based functional connectivity reflects cognitive load during emotion regulation. In: Proceedings of 2016 IEEE 13th International Symposium on Biomedical Imaging, pp. 771–774, Prague, Czech Republic. https://doi.org/10.1109/ISBI.2016.7493380
Wu J, Zhang J, Ding X, Li R, Zhou C (2013) The effects of music on brain functional networks: a network analysis. Neurosci 250:49–59. https://doi.org/10.1016/j.neuroscience.2013.06.021
Li Y (2017) Emotion analysis and recognition based on EEG brain networks. MSc Thesis Taiyuan University of Technology, Taiyuan. https://kns.cnki.net/kcms/detail/detail.aspx?dbcode=CMFD&dbname=CMFD201801&filename=1017832642.nh&v=1VksciZ9seB1BljMWHN%25mmd2B3cbIloS69OYbBF55%25mmd2FI%25mmd2Fy54xMELX8vLPR3aq25Sb8z0lo. Accessed 16th, December, 2017-15th, January, 2018
Wu Z (2015) The research of EEG brain function network construction and application. MSc Thesis Taiyuan University of Technology, Taiyuan. https://kns.cnki.net/kcms/detail/detail.aspx?dbcode=CMFD&dbname=CMFD201502&filename=1015603212.nh&v=rRg%25mmd2B175q9Zw%25mmd2B7gIKvSBRXwPuGN8IR7J7FAOtuI4HyMgujyG9SqqMAwkEiBuFHY62. Accessed 16th, August, 2015-15th, September, 2015
Gao J, Wang W (2015) Research of effective network of emotion electroencephalogram based on sparse Bayesian network. J Biomed Eng 32(5): 945–951. https://schlr.cnki.net/zn/Detail/index/SJPD_04/SJPD421BF131D7A7B399759FB0E904610D38. Accessed October, 2015
Zhang J, Zhao S, Huang W, Hu S (2017) Brain effective connectivity analysis from EEG for positive and negative emotion. In: Proceedings of International Conference on Neural Information Processing, pp. 851–857, Guangzhou, China. https://doi.org/10.1007/978-3-319-70093-9_90
Sreeshakthy M, Preethi J (2016) Classification of human emotion from deap EEG signal using hybrid improved neural networks with cuckoo search. Brain Broad Res Artif Intell Neurosci 6(3–4):60–73
Li Q, Cao D, Li Y, Tang Y (2017) Research on the effects of the continuous theta burst transcranial magnetic stimuli on brain network in emotional processing. J Biomed Eng 34(4):518–528. https://doi.org/10.7507/1001-5515.201606048
McPherson MJ, Barrett FS, Lopez-Gonzalez M, Jiradejvong P, Limb CJ (2016) Emotional intent modulates the neural substrates of creativity: an fMRI study of emotionally targeted improvisation in jazz musicians. Sci Rep 6:18460. https://doi.org/10.1038/srep18460
Koelstra S, Muhl C, Soleymani M et al (2012) DEAP: a database for emotion analysis; using physiological signals. IEEE T Affect Comput 3:18–31. https://doi.org/10.1109/T-AFFC.2011.15
Du N, Zhou F, Pulver EM, Tilbury DM, Robert LP, Pradhan AK, Yang XJ (2020) Examining the effects of emotional valence and arousal on takeover performance in conditionally automated driving. Transp Res Pt C-Emerg Technol 112:78–87. https://doi.org/10.1016/j.trc.2020.01.006
Morris JD (1995) SAM: The self-assessment manikin an efficient cross-cultural measurement of emotional response. J Advert Res 35(8):63–68
He G, Hu Y, Yang Y, Wei W (2015) Construction and analysis of brain functionality network based on rs-fMRI data. J East China Univ Sci Technol (Nat Sci Edit) 41:821–827. https://doi.org/10.14135/j.cnki.1006-3080.2015.06.015
Rubinov M, Sporns O (2010) Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52:1059–1069. https://doi.org/10.1016/j.neuroimage.2009.10.003
Bordier C, Nicolini C, Bifone A (2017) Graph analysis and modularity of brain functional connectivity networks: searching for the optimal threshold. Front Neurosci 11:441. https://doi.org/10.3389/fnins.2017.00441
Liao X, Vasilakos AV, He Y (2017) Small-world human brain networks: perspectives and challenges. Neurosci Biobehav R 77:286–300. https://doi.org/10.1016/j.neubiorev.2017.03.018
Gouveia L, Martins P (2015) Solving the maximum edge-weight clique problem in sparse graphs with compact formulations. EURO J Comput Optim 3(1):1–30. https://doi.org/10.1007/s13675-014-0028-1
Jiao Y (2014) Phase synchronization model and its applications. MSc Thesis Xidian University, Xi’an. https://kns.cnki.net/kcms/detail/detail.aspx?dbcode=CMFD&dbname=CMFD201402&filename=1014330880.nh&v=LZGMdserltfV%25mmd2BIWalsgtmIhlGSz2IKFQeVdnxebZ8j2uDFS%25mmd2B4TjNGxpoYs5h1XfH. Accessed 16th, October, 2014-15th, November, 2014
Sun J, Li Z, Tong S (2012) Inferring functional neural connectivity with phase synchronization analysis: a review of methodology. Comput Math Method Med 2012:239210. https://doi.org/10.1155/2012/239210
Sakkalis V (2011) Review of advanced techniques for the estimation of brain connectivity measured with EEG/MEG. Comput Biol Med 41(12):1110–1117. https://doi.org/10.1016/j.compbiomed.2011.06.020
Xu H, Plataniotis KN (2017) Affective states classification using EEG and semi-supervised deep learning approaches. In: Proceedings of the International Workshop on Multimedia Signal Processing, pp. 1–6, London, UK.
Zhang J, Chen M, Hu S, Cao Y, Kozma R (2016) PNN for EEG-based emotion recognition. In: Proceedings of the 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 2319–2323, Budapest, Hungary.
Lachaux JP, Rodriguez E, Quyen MLV et al (2000) Studying single-trials of phase synchronous activity in the brain. Int J Bifurcat Chaos 10:2429–2439. https://doi.org/10.1142/S0218127400001560
Lachaux JP, Rodriguez E, Martinerie J, Varela FJ (1999) Measuring phase synchrony in brain signals. Hum Brain Mapp 8:194–208. https://doi.org/10.1002/(SICI)1097-0193(1999)8:4%3c194::AID-HBM4%3e3.0.CO;2-C
Wang Z, Tong Y, Heng X (2019) Phase-locking value based graph convolutional neural networks for emotion recognition. IEEE Access 7:93711–93722. https://doi.org/10.1109/ACCESS.2019.2927768
Zhang J, Xu H, Zhu L, Kong W, Ma Z (2019) Gender recognition in emotion perception using EEG features, In: Proceedings of the 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 2883–2887. https://doi.org/10.1109/BIBM47256.2019.8983332.
Sun J, Hong X, Tong S (2012) Phase synchronization analysis of EEG signals: an evaluation based on surrogate tests. IEEE Trans Biomed Eng 59: 2254–2263, San Diego, USA. https://doi.org/10.1109/TBME.2012.2199490
Guo H (2013) Machine learning classifier using abnormal resting state functional brain network topological metrics in major depressive disorder. PhD Dissertation Taiyuan University of Technology, Taiyuan. https://kns.cnki.net/kcms/detail/detail.aspx?dbcode=CDFD&dbname=CDFD1214&filename=1014155588.nh&v=E9oluIozrv%25mmd2FUjG%25mmd2FDZ5rcckjggQenxWs%25mmd2B9%25mmd2BHU4kew2vJL4CEvwV5zt%25mmd2FTklVy4LAAQ. Accessed 16th, May, 2014-15th, June, 2014
Keselman HJ, Keselman JC, Games PA (1991) Maximum familywise type I error rate: the least significant difference, Newman-Keuls, and other multiple comparison procedures. Psychol Bull 110:155–161. https://doi.org/10.1037/0033-2909.110.1.155
Zimmerman DW (2004) Inflation of type I error rates by unequal variances associated with parametric, nonparametric, and Rank-Transformation Tests. Psicológica, 25: 103–133. http://www.redalyc.org/articulo.oa?id=16925106
Rosenthal R (1994) Parametric measures of effect size. The handbook of research synthesis 231-244
Chaudhry A, Xu P, Gu Q (2017) Uncertainty assessment and false discovery rate control in high-dimensional Granger causal inference. In: Proceedings of the 34th International Conference on Machine Learning, PMLR 70:684–693, Sydney, Australia. https://proceedings.mlr.press/v70/chaudhry17a.html. Accessed 6th, August 2017
Wang Y, Zhai J, Wu X, Adu-Gyamfi EA et al (2022) LncRNA functional annotation with improved false discovery rate achieved by disease associations. Comp Struct Biotechnol J 20:322–332. https://doi.org/10.1016/j.csbj.2021.12.016
Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B 57:289–300
Benditkis J, Heesen P, Janssen A (2018) The false discovery rate (FDR) of multiple tests in a class room lecture. Stat Probab Lett 134:29–35. https://doi.org/10.1016/j.spl.2017.09.017
Funding
This work was sponsored by National Natural Science Foundation of China (Grant No. 61301012, No. 61471140), Sci-tech Innovation Foundation of Harbin (No. 2016RALGJ001), and China Scholarship Council.
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Liu, D., Cao, T., Wang, Q. et al. Construction and analysis of functional brain network based on emotional electroencephalogram. Med Biol Eng Comput 61, 357–385 (2023). https://doi.org/10.1007/s11517-022-02708-8
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DOI: https://doi.org/10.1007/s11517-022-02708-8