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Visualization and exploration of multichannel EEG coherence networksJi, Chengtao

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B I B L I O G R A P H Y

[1] S. Achard. A resilient, low-frequency, small-world human brainfunctional network with highly connected association corticalhubs. Journal of Neuroscience, 26(1):63–72, 2006. doi 10.1523/JNEUROSCI.3874-05.2006. url http://www.jneurosci.org/cgi/doi/10.1523/JNEUROSCI.3874-05.2006.

[2] M. Ahmadlou and H. Adeli. Functional community analysisof brain: A new approach for EEG-based investigation of thebrain pathology. NeuroImage, 58(2):401–408, 2011. doi 10.1016/j.neuroimage.2011.04.070. url http://dx.doi.org/10.1016/j.neuroimage.2011.04.070.

[3] J. Ahn, C. Plaisant, and B. Shneiderman. A task taxonomy fornetwork evolution analysis. IEEE Transactions on Visualizationand Computer Graphics, 20(3):365–376, mar 2014. doi 10.1109/tvcg.2013.238. url https://doi.org/10.1109%2Ftvcg.2013.238.

[4] B. Alper, B. Bach, N. Henry Riche, T. Isenberg, and J. D. Fekete.Weighted graph comparison techniques for brain connectivityanalysis. In Proceedings of the SIGCHI Conference on HumanFactors in Computing Systems, CHI ’13, pages 483–492, NewYork, NY, USA, 2013. ACM. isbn 978-1-4503-1899-0. doi10.1145/2470654.2470724. url http://doi.acm.org/10.1145/2470654.2470724.

[5] D. Archambault, H. Purchase, and B. Pinaud. Animation, smallmultiples, and the e�ect of mental map preservation in dynamicgraphs. IEEE Transactions on Visualization and Computer Graph-ics, 17(4):539–552, 2011. doi 10.1109/TVCG.2010.78.

[6] B. Bach, N. Henry Riche, T. Dwyer, T. Madhyastha, J. D. Fekete,and T. Grabowski. Small MultiPiles: Piling Time to Explore Tem-poral Patterns in Dynamic Networks. Computer Graphics Fo-rum, 34(3):31–40, 2015. doi 10.1111/cgf.12615. url http://doi.wiley.com/10.1111/cgf.12615.

[7] C. Büchel, C. Frith, and K. Friston. Functional integration:methods for assessing interactions among neuronal systems us-ing brain imaging. In The Neurocognition of Language, pages337–358. Oxford University Press, jul 2000. doi 10.1093/acprof:oso/9780198507932.003.0011. url https://doi.org/10.1093%2Facprof%3Aoso%2F9780198507932.003.0011.

107

bibliography

[8] F. Beck, M. Burch, S. Diehl, and D. Weiskopf. The State of the Artin Visualizing Dynamic Graphs. In EuroVis - STARs, Swansea,Wales, UK, 2014. The Eurographics Association. isbn 978-3-03868-028-4. doi 10.2312/eurovisstar.20141174.

[9] F. Beck, M. Burch, S. Diehl, and D. Weiskopf. A taxonomyand survey of dynamic graph visualization. Computer Graph-ics Forum, 36(1):133–159, jan 2016. doi 10.1111/cgf.12791. urlhttps://doi.org/10.1111%2Fcgf.12791.

[10] J. Blaas, C. Botha, B. Peters, F. Vos, and F. Post. Fast and repro-ducible �ber bundle selection in DTI visualization. In IEEE Visu-alization 2005 - (VIS'05). IEEE, 2005. doi 10.1109/vis.2005.40. urlhttps://doi.org/10.1109%2Fvis.2005.40.

[11] V. D. Blondel, J. L. Guillaume, R. Lambiotte, and E. Lefebvre. Fastunfolding of communities in large networks. Journal of StatisticalMechanics: Theory and Experiment, 10008(10):6, 2008. doi 10.1088/1742-5468/2008/10/P10008. url http://arxiv.org/abs/0803.0476.

[12] I. Boyandin, E. Bertini, and D. Lalanne. A qualitative study on theexploration of temporal changes in �ow maps with animationand small-multiples. Computer Graphics Forum, 31(3pt2):1005–1014, jun 2012. doi 10.1111/j.1467-8659.2012.03093.x. url https://doi.org/10.1111%2Fj.1467-8659.2012.03093.x.

[13] C. Bron and J. Kerbosch. Algorithm 457: Finding All Cliques ofan Undirected Graph. Commun. ACM, 16(9):575–577, 1973. doi10.1145/362342.362367. url http://doi.acm.org/10.1145/362342.362367.

[14] A. Buja, D. F. SWAYNE, M. L. Littman, N. Dean, H. Hofmann, andL. Chen. Data Visualization with Multidimensional Scaling. Jour-nal of Computational and Graphical Statistics, 17(2):444–472, 2008.doi DOI:10.1198/106186008X318440. url http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.11.4365.

[15] E. Bullmore and O. Sporns. Complex brain networks: graph the-oretical analysis of structural and functional systems. NatureReviews Neuroscience, 10(4):312–312, 2009. doi 10.1038/nrn2618.url http://www.nature.com/doifinder/10.1038/nrn2618.

[16] M. ten Caat, N. M. Maurits, and J. B. T. M. Roerdink. Functionalunit maps for data-driven visualization of high-density EEG co-herence. In Proc. Eurographics/IEEE VGTC Symposium on Visual-ization (EuroVis), pages 259–266, 2007.

108

bibliography

[17] M. ten Caat, N. M. Maurits, and J. B. T. M. Roerdink. Watershed-based visualization of high-density EEG coherence. In G. J. F.Banon, J. Barrera, and U. de Mendoca Braga-Neto, editors, Proc.8th International Symposium on Mathematical Morphology, Rio deJaneiro, pages 289–300, Oct 10-13 2007.

[18] M. ten Caat. Fumaplab: multichannel EEG Matlab toolbox,2008. http://www.cs.rug.nl/~roe/software/FuMapLab/FuMapLab0-2.tgz.

[19] M. ten Caat, M. M. Lorist, E. Bezdan, J. B. T. M. Roerdink, andN. M. Maurits. High-density EEG coherence analysis using func-tional units applied to mental fatigue. Journal of NeuroscienceMethods, 171(2):271–278, 2008. doi 10.1016/j.jneumeth.2008.03.022. url http://linkinghub.elsevier.com/retrieve/pii/S0165027008002033.

[20] M. ten Caat, N. M. Maurits, and J. B. T. M. Roerdink. Data-drivenvisualization and group analysis of multichannel EEG coherencewith functional units. IEEE Transactions on Visualization andComputer Graphics, 14(4):756–771, 2008. doi 10.1109/TVCG.2008.21.

[21] T. S. Caetano, J. J. McAuley, L. Cheng, Q. V. Le, and A. J. Smola.Learning graph matching. IEEE transactions on pattern analysisand machine intelligence, 31(6):1048–1058, 2009.

[22] E. G. Christodoulou, V. Sakkalis, V. Tsiaras, and I. G. Tollis. Brain-NetVis: An open-access tool to e�ectively quantify and visual-ize brain networks. Computational Intelligence and Neuroscience,2011:1–12, 2011. doi 10.1155/2011/747290. url https://doi.org/10.1155%2F2011%2F747290.

[23] L. d. F. Costa, F. A. Rodrigues, G. Travieso, and P. R. Villas Boas.Characterization of complex networks: A survey of measure-ments. Advances in physics, 56(1):167–242, 2007.

[24] N. M. da Costa, D. Fürsinger, and K. A. Martin. The synaptic or-ganization of the claustral projection to the cat’s visual cortex.Journal of Neuroscience, 30(39):13166–13170, 2010.

[25] A. Crippa and J. B. T. M. Roerdink. Data-driven visualization offunctional brain regions from resting state fMRI data. In P. Eisert,K. Polthier, and J. Hornegger, editors, Proceedings Vision, Model-ing and Visualization Workshop (VMV), 4-6 Oct, Berlin, 2011. doihttp://dx.doi.org/10.2312/PE/VMV/VMV11/247-254. url http://dx.doi.org/10.2312/PE/VMV/VMV11/247-254.

109

bibliography

[26] A. Crippa, N. M. Maurits, M. M. Lorist, and J. B. T. M. Roerdink.Graph averaging as a means to compare multichannel EEG co-herence networks and its application to the study of mental fa-tigue and neurodegenerative disease. Computers & Graphics, 35(2):265–274, apr 2011. doi 10.1016/j.cag.2010.12.008. url https://doi.org/10.1016%2Fj.cag.2010.12.008.

[27] L. Danon, A. Díaz-Guilera, J. Duch, and A. Arenas. Comparingcommunity structure identi�cation. Journal of Statistical Mechan-ics: Theory and Experiment, 2005(09):P09008–P09008, sep 2005.doi 10.1088/1742-5468/2005/09/p09008. url https://doi.org/10.1088%2F1742-5468%2F2005%2F09%2Fp09008.

[28] G. B. Dantzig. Application of the Simplex Method to a Transporta-tion Problem - Ch XXIII, 1951. url http://cowles.econ.yale.edu/P/cm/m13/m13-23.pdf.

[29] S. Diel, C. Görg, and A. Kerren. Preserving the Mental Map usingForesighted Layout. In Eurographics / IEEE VGTC Symposium onVisualization, Ascona, Switzerland, 2001. The Eurographics As-sociation. isbn 3-211-83674-8. doi 10.2312/VisSym/VisSym01/175-184.

[30] E. van Diessen, T. Numan, E. van Dellen, A. van der Kooi,M. Boersma, D. Hofman, R. van Lutterveld, B. van Dijk, E. vanStraaten, A. Hillebrand, and C. Stam. Opportunities and method-ological challenges in EEG and MEG resting state functionalbrain network research. Clinical Neurophysiology, 126(8):1468–1481, aug 2015. doi 10.1016/j.clinph.2014.11.018. url https://doi.org/10.1016%2Fj.clinph.2014.11.018.

[31] M. B. Dillencourt, D. Eppstein, and M. T. Goodrich. Choosing col-ors for geometric graphs via color space embeddings. In Inter-national Symposium on Graph Drawing, pages 294–305. Springer,2006.

[32] M. B. Dillencourt, D. Eppstein, and M. T. Goodrich. Choosingcolors for geometric graphs via color space embeddings. InGraph Drawing, pages 294–305. Springer Berlin Heidelberg, 2007.doi 10.1007/978-3-540-70904-6_29. url https://doi.org/10.1007%2F978-3-540-70904-6_29.

[33] N. U. F. Dosenbach, B. Nardos, A. L. Cohen, D. A. Fair, J. D. Power,J. A. Church, S. M. Nelson, G. S. Wig, A. C. Vogel, C. N. Lessov-Schlaggar, K. A. Barnes, J. W. Dubis, E. Feczko, R. S. Coalson, J. R.Pruett, D. M. Barch, S. E. Petersen, and B. L. Schlaggar. Predictionof individual brain maturity using fMRI. Science, 329(5997):1358–1361, sep 2010. doi 10.1126/science.1194144. url https://doi.org/10.1126%2Fscience.1194144.

110

bibliography

[34] S. van den Elzen, D. Holten, J. Blaas, and J. J. van Wijk. Dy-namic network visualization withExtended massive sequenceviews. IEEE Transactions on Visualization and Computer Graph-ics, 20(8):1087–1099, aug 2014. doi 10.1109/tvcg.2013.263. urlhttps://doi.org/10.1109%2Ftvcg.2013.263.

[35] S. van den Elzen, D. Holten, J. Blaas, and J. J. van Wijk. Re-ducing snapshots to points: A visual analytics approach to dy-namic network exploration. IEEE Transactions on Visualizationand Computer Graphics, 22(1):1–10, jan 2016. doi 10.1109/tvcg.2015.2468078. url https://doi.org/10.1109%2Ftvcg.2015.2468078.

[36] M. D. Fairchild. Color Appearance Models. John Wiley & Sons,Ltd, jun 2013. doi 10.1002/9781118653128. url https://doi.org/10.1002%2F9781118653128.

[37] A. Ford and A. Roberts. Colour space conversions. WestminsterUniversity, London, 1998:1–31, 1998. url https://poynton.ca/PDFs/coloureq.pdf.

[38] S. Fortunato. Community detection in graphs. Physics Reports,486(3-5):75–174, 2010. doi 10.1016/j.physrep.2009.11.002.

[39] K. J. Friston, C. D. Frith, and R. S. J. Frackowiak. Time-dependentchanges in e�ective connectivity measured with PET. HumanBrain Mapping, 1(1):69–79, 1993. doi 10.1002/hbm.460010108.url https://doi.org/10.1002%2Fhbm.460010108.

[40] K. J. Friston, C. D. Frith, P. F. Liddle, and R. S. J. Frackowiak. Func-tional connectivity: The principal-component analysis of large(PET) data sets. Journal of Cerebral Blood Flow & Metabolism, 13(1):5–14, jan 1993. doi 10.1038/jcbfm.1993.4. url https://doi.org/10.1038%2Fjcbfm.1993.4.

[41] K. J. Friston. Functional and e�ective connectivity in neuroimag-ing: A synthesis. Human Brain Mapping, 2(1-2):56–78, 1994.doi 10.1002/hbm.460020107. url https://doi.org/10.1002%2Fhbm.460020107.

[42] T. M. J. Fruchterman and E. M. Reingold. Graph drawing by force-directed placement. Software: Practice and Experience, 21(11):1129–1164, 1991. doi 10.1002/spe.4380211102.

[43] T. Fujiwara, J. K. Chou, A. M. McCullough, C. Ranganath, and K. L.Ma. A visual analytics system for brain functional connectivitycomparison across individuals, groups, and time points. In 2017IEEE Paci�c Visualization Symposium (Paci�cVis). IEEE, apr 2017.doi 10.1109/paci�cvis.2017.8031601. url https://doi.org/10.1109%2Fpacificvis.2017.8031601.

111

bibliography

[44] E. R. Gansner, Y. Koren, and S. North. Graph drawing by stressmajorization. In Graph Drawing, pages 239–250. Springer BerlinHeidelberg, 2005. doi 10.1007/978-3-540-31843-9_25. url https://doi.org/10.1007%2F978-3-540-31843-9_25.

[45] X. Gao, B. Xiao, D. Tao, and X. Li. A survey of graph edit distance.Pattern Analysis and Applications, 13(1):113–129, 2010. doi 10.1007/s10044-008-0141-y.

[46] S. Gerhard, A. Daducci, A. Lemkaddem, R. Meuli, J. P. Thiran, andP. Hagmann. The connectome viewer toolkit: An open sourceframework to manage, analyze, and visualize connectomes. Fron-tiers in Neuroinformatics, 5, 2011. doi 10.3389/fninf.2011.00003.url https://doi.org/10.3389%2Ffninf.2011.00003.

[47] M. Ghoniem, J. D. Fekete, and P. Castagliola. A comparison ofthe readability of graphs using node-link and matrix-based rep-resentations. In IEEE Symposium on Information Visualization.IEEE, 2004. doi 10.1109/infvis.2004.1. url https://doi.org/10.1109%2Finfvis.2004.1.

[48] M. Ghoniem, J. D. Fekete, and P. Castagliola. On the readability ofgraphs using node-link and matrix-based representations: A con-trolled experiment and statistical analysis. Information Visualiza-tion, 4(2):114–135, July 2005. doi 10.1057/palgrave.ivs.9500092.url http://dx.doi.org/10.1057/palgrave.ivs.9500092.

[49] A. Gisbrecht and B. Hammer. Data visualization by nonlin-ear dimensionality reduction. Wiley Interdisciplinary Reviews:Data Mining and Knowledge Discovery, 5(2):51–73, feb 2015. doi10.1002/widm.1147. url https://doi.org/10.1002%2Fwidm.1147.

[50] T. E. Gladwin, J. P. Lindsen, and R. De Jong. Pre-stimulus EEGe�ects related to response speed, task switching and upcomingresponse hand. Biological Psychology, 72(1):15–34, 2006. doi 10.1016/j.biopsycho.2005.05.005.

[51] D. Greene, D. Doyle, and P. Cunningham. Tracking the evolutionof communities in dynamic social networks. In Proceedings ofthe 2010 International Conference on Advances in Social NetworksAnalysis and Mining, pages 176–183, Washington, DC, USA, 2010.IEEE Computer Society. isbn 978-0-7695-4138-9. doi 10.1109/ASONAM.2010.17. url http://dx.doi.org/10.1109/ASONAM.2010.17.

[52] P. Hagmann, L. Cammoun, X. Gigandet, R. Meuli, C. J. Honey,V. J. Wedeen, and O. Sporns. Mapping the structural core ofhuman cerebral cortex. PLoS Biology, 6(7):e159, jul 2008. doi

112

bibliography

10.1371/journal.pbio.0060159. url https://doi.org/10.1371%2Fjournal.pbio.0060159.

[53] D. Halliday, J. Rosenberg, A. Amjad, P. Breeze, B. Conway, andS. Farmer. A framework for the analysis of mixed time series/-point process data—theory and application to the study of phys-iological tremor, single motor unit discharges and electromyo-grams. Progress in Biophysics and Molecular Biology, 64(2):237–278, 1995. doi 10.1016/s0079-6107(96)00009-0. url https://doi.org/10.1016%2Fs0079-6107%2896%2900009-0.

[54] M. Harrower and C. A. Brewer. ColorBrewer.org: An online toolfor selecting colour schemes for maps. The Cartographic Jour-nal, 40(1):27–37, jun 2003. doi 10.1179/000870403235002042. urlhttps://doi.org/10.1179%2F000870403235002042.

[55] M. Hassan, M. Shamas, M. Khalil, W. E. Falou, and F. Wendling.EEGNET: An open source tool for analyzing and visualizingM/EEG connectome. PLOS ONE, 10(9):e0138297, sep 2015. doi 10.1371/journal.pone.0138297. url https://doi.org/10.1371%2Fjournal.pone.0138297.

[56] N. Henry and J. D. Fekete. MatrixExplorer: a dual-representationsystem to explore social networks. IEEE Transactions on Visual-ization and Computer Graphics, 12(5):677–684, sep 2006. doi 10.1109/tvcg.2006.160. url https://doi.org/10.1109%2Ftvcg.2006.160.

[57] N. Henry and J. D. Fekete. MatLink: Enhanced matrix visual-ization for analyzing social networks. In Lecture Notes in Com-puter Science, pages 288–302. Springer Berlin Heidelberg, 2007.doi 10.1007/978-3-540-74800-7_24. url https://doi.org/10.1007%2F978-3-540-74800-7_24.

[58] J. Holsheimer and B. W. A. Feenstra. Volume conduction and EEGmeasurements within the brain. a quantitative approach to the in-�uence of electrical spread on the linear relationship of activitymeasured at di�erent locations. Electroencephalography and clin-ical neurophysiology, 43:52–58, 1977.

[59] C. J. Honey, R. Kotter, M. Breakspear, and O. Sporns. Net-work structure of cerebral cortex shapes functional connec-tivity on multiple time scales. Proceedings of the NationalAcademy of Sciences, 104(24):10240–10245, 2007. doi 10.1073/pnas.0701519104. url http://www.pnas.org/cgi/doi/10.1073/pnas.0701519104.

[60] S. G. Hormuzdi, M. A. Filippov, G. Mitropoulou, H. Monyer, andR. Bruzzone. Electrical synapses: a dynamic signaling system

113

bibliography

that shapes the activity of neuronal networks. Biochimica et Bio-physica Acta (BBA) - Biomembranes, 1662(1-2):113–137, mar 2004.doi 10.1016/j.bbamem.2003.10.023. url https://doi.org/10.1016%2Fj.bbamem.2003.10.023.

[61] Y. Hu, S. G. Kobourov, and S. Veeramoni. Embedding, cluster-ing and coloring for dynamic maps. In 2012 IEEE Paci�c Vi-sualization Symposium. IEEE, feb 2012. doi 10.1109/paci�cvis.2012.6183571. url https://doi.org/10.1109%2Fpacificvis.2012.6183571.

[62] C. Hurter, O. Ersoy, and A. Telea. Smooth bundling of largestreaming and sequence graphs. In 2013 IEEE Paci�c VisualizationSymposium (Paci�cVis). IEEE, feb 2013. doi 10.1109/paci�cvis.2013.6596126. url https://doi.org/10.1109%2Fpacificvis.2013.6596126.

[63] A. A. Ioannides. Dynamic functional connectivity. Current Opin-ion in Neurobiology, 17(2):161–170, apr 2007. doi 10.1016/j.conb.2007.03.008. url https://doi.org/10.1016%2Fj.conb.2007.03.008.

[64] D. Jäckle, F. Fischer, T. Schreck, and D. A. Keim. Temporal MDSPlots for Analysis of Multivariate Data. IEEE Transactions onVisualization and Computer Graphics, 22(1):141–150, 2016. doi10.1109/TVCG.2015.2467553.

[65] C. Ji. Dynamic coherence networks visualization demostration,2017. https://DynCohNetVis.github.io/.

[66] C. Ji, J. J. van de Gronde, N. M. Maurits, and J. B. T. M. Roerdink.Tracking and Visualizing Dynamic Structures in MultichannelEEG Coherence Networks. In T. Isenberg and F. Sadlo, edi-tors, EuroVis 2016 - Posters. The Eurographics Association, 2016.isbn 978-3-03868-015-4. doi 10.2312/eurp.20161134. url http://dx.doi.org/10.2312/eurp.20161134.

[67] C. Ji, J. J. van de Gronde, N. M. Maurits, and J. B. T. M. Roerdink.Exploration of Complex Dynamic Structures in MultichannelEEG Coherence Networks via Information Visualization Tech-niques. In CCS 2016 - Posters, 2016.

[68] C. Ji, J. J. van de Gronde, N. M. Maurits, and J. B. T. M. Roerdink.Visualizing and Exploring Dynamic Multichannel EEG Coher-ence Networks. In S. Bruckner, A. Hennemuth, B. Kainz, I. Hotz,D. Merhof, and C. Rieder, editors, Eurographics Workshop on Vi-sual Computing for Biology andMedicine (VCBM). The Eurograph-ics Association, 2017. isbn 978-3-03868-036-9. doi 10.2312/vcbm.20171238. url http://dx.doi.org/10.2312/vcbm.20171238.

114

bibliography

[69] C. Ji, N. M. Maurits, and J. B. T. M. Roerdink. Visualization of mul-tichannel EEG coherence networks based on community struc-ture analysis. In Studies in Computational Intelligence, pages583–594. Springer International Publishing, Nov 2017. isbn 978-3-319-72150-7. doi 10.1007/978-3-319-72150-7_47. url https://doi.org/10.1007%2F978-3-319-72150-7_47.

[70] R. Jianu, A. Rusu, A. J. Fabian, and D. H. Laidlaw. A coloringsolution to the edge crossing problem. In Information visualisa-tion, 2009 13th international conference, pages 691–696. IEEE, 2009.isbn 9780769537337. doi 10.1109/IV.2009.66.

[71] M. Kaminski, K. Blinowska, and W. Szclenberger. Topographicanalysis of coherence and propagation of EEG activity duringsleep and wakefulness. Electroencephalography and Clinical Neu-rophysiology, 102(3):216–227, 1997. doi 10.1016/S0013-4694(96)95721-5. url http://dx.doi.org/10.1016/S0013-4694(96)95721-5.

[72] S. Kaski and J. Peltonen. Dimensionality reduction for data visual-ization [applications corner]. IEEE Signal Processing Magazine, 28(2):100–104, 2011. doi https://doi.org/10.1109/MSP.2010.940003.

[73] A. Kirschner, J. W. Y. Kam, T. C. Handy, and L. M. Ward. Di�er-ential synchronization in default and task-speci�c networks ofthe human brain. Frontiers in Human Neuroscience, 6, 2012. doi10.3389/fnhum.2012.00139. url https://doi.org/10.3389%2Ffnhum.2012.00139.

[74] J. P. Lachaux, E. Rodriguez, J. Martinerie, and F. J. Varela. Measur-ing phase synchrony in brain signals. Human Brain Mapping,8(4):194–208, 1999. doi 10.1002/(SICI)1097-0193(1999)8:4<194::AID-HBM4>3.0.CO;2-C.

[75] T. von Landesberger, A. Kuijper, T. Schreck, J. Kohlhammer,J. van Wijk, J. D. Fekete, and D. Fellner. Visual analysis of largegraphs: State-of-the-art and future research challenges. Com-puter Graphics Forum, 30(6):1719–1749, apr 2011. doi 10.1111/j.1467-8659.2011.01898.x. url https://doi.org/10.1111%2Fj.1467-8659.2011.01898.x.

[76] K. Li, L. Guo, C. Faraco, D. Zhu, H. Chen, Y. Yuan, J. Lv,F. Deng, X. Jiang, T. Zhang, et al. Visual analytics of brain net-works. NeuroImage, 61(1):82–97, 2012. doi http://doi.org/10.1016/j.neuroimage.2012.02.075.

[77] G. A. Light, L. E. Williams, F. Minow, J. Sprock, A. Rissling,R. Sharp, N. R. Swerdlow, and D. L. Bra�. Electroencephalography(eeg) and event-related potentials (erps) with human participants.Current protocols in neuroscience, pages 6–25, 2011.

115

bibliography

[78] S. Liu, Y. Wu, E. Wei, M. Liu, and Y. Liu. StoryFlow: Trackingthe evolution of stories. IEEE Transactions on Visualization andComputer Graphics, 19(12):2436–2445, 2013. doi 10.1109/TVCG.2013.196.

[79] L. Livi and A. Rizzi. The graph matching problem. PatternAnalysis and Applications, 16(3):253–283, 2013. doi 10.1007/s10044-012-0284-8.

[80] M. M. Lorist, E. Bezdan, M. ten Caat, M. M. Span, J. B. T. M.Roerdink, and N. M. Maurits. The in�uence of mental fatigueand motivation on neural network dynamics; an EEG coher-ence study. Brain Research, 1270:95–106, 2009. doi 10.1016/j.brainres.2009.03.015. url http://linkinghub.elsevier.com/retrieve/pii/S0006899309005162.

[81] S. J. Luck. An Introduction to the Event-Related Potential Tech-nique. Monographs of the Society for Research in Child Develop-ment, 78(3):388, 2005. doi 10.1118/1.4736938.

[82] S. J. Luck. An Introduction to the Event-related Potential Technique.An Introduction to the Event-related Potential Technique. MITPress, 2005. isbn 9780262621960. url https://books.google.nl/books?id=r-BqAAAAMAAJ.

[83] C. Ma, R. V. Kenyon, A. G. Forbes, T. Berger-Wolf, B. J. Slater,and D. A. Llano. Visualizing Dynamic Brain Networks Using anAnimated Dual-Representation. In E. Bertini, J. Kennedy, andE. Puppo, editors, Eurographics Conference on Visualization (Eu-roVis) - Short Papers. The Eurographics Association, 2015. doi10.2312/eurovisshort.20151128. url http://dx.doi.org/10.2312/eurovisshort.20151128.

[84] L. V. D. Maaten, G. Hinton, and H. G. van der Maaten L. Visualiz-ing data using t-SNE. J. Mach. Learn. Res., 9:2579–2605, 2008. doi10.1007/s10479-011-0841-3.

[85] O. Macindoe and W. Richards. Graph Comparison Using FineStructure Analysis. 2010 IEEE Second International Conference onSocial Computing, pages 193–200, 2010. doi 10.1109/SocialCom.2010.35. url http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5590440.

[86] N. M. Maurits, R. Scheeringa, J. H. van der Hoeven, and R. de Jong.EEG coherence obtained from an auditory oddball task increaseswith age. Journal of clinical neurophysiology, 23(5):395–403,2006. doi 10.1097/01.wnp.0000219410.97922.4e. url http://www.ncbi.nlm.nih.gov/pubmed/17016149.

116

bibliography

[87] A. Mheich, M. Hassan, V. Gripon, M. Khalil, C. Berrou, O. Dufor,and F. Wendling. A novel algorithm for measuring graph similar-ity: Application to brain networks. International IEEE/EMBS Con-ference on Neural Engineering, NER, 2015-July:1068–1071, 2015.doi 10.1109/NER.2015.7146812.

[88] K. Misue, P. Eades, W. Lai, and K. Sugiyama. LayoutAdjustment and the Mental Map. Journal of Visual Lan-guages & Computing, 6(2):183–210, 1995. doi 10.1006/jvlc.1995.1010. url http://www.sciencedirect.com/science/article/pii/S1045926X85710105.

[89] S. Mori, B. J. Crain, V. P. Chacko, and P. C. Van Zijl. Three-dimensional tracking of axonal projections in the brain by mag-netic resonance imaging. Annals of Neurology: O�cial Journalof the American Neurological Association and the Child NeurologySociety, 45(2):265–269, 1999.

[90] T. Munzner. Visualization Analysis and Design. New York: A KPeters/CRC Press., dec 2014. doi 10.1201/b17511. url https://doi.org/10.1201%2Fb17511.

[91] T. Munzner. A nested model for visualization design and vali-dation. IEEE Transactions on Visualization and Computer Graph-ics, 15(6):921–928, nov 2009. doi 10.1109/tvcg.2009.111. urlhttps://doi.org/10.1109%2Ftvcg.2009.111.

[92] S. M. Nelson, A. L. Cohen, J. D. Power, G. S. Wig, M. Francis, M. E.Wheeler, K. Velanova, D. I. Donaldson, S. Je�rey, B. L. Schlaggar,S. E. Petersen, R. F. Trial, L. S. Fuchs, S. R. Powell, C. L. Hamlett,D. Fuchs, P. T. Cirino, and J. M. Fletcher. A parcellation schemefor human left lateral parietal cortex. Research in Education, 67(1):156–170, 2011. doi 10.1016/j.neuron.2010.05.025.A.

[93] M. E. J. Newman. Fast algorithm for detecting community struc-ture in networks. Physical Review E - Statistical, Nonlinear, andSoft Matter Physics, 69(6 2):1–5, 2004. doi 10.1103/PhysRevE.69.066133.

[94] M. E. J. Newman. Finding community structure in networks us-ing the eigenvectors of matrices. Physical Review E - Statisti-cal, Nonlinear, and Soft Matter Physics, 74(3):1–19, 2006. doi10.1103/PhysRevE.74.036104.

[95] M. E. J. Newman and M. Girvan. Finding and evaluating commu-nity structure in networks. Physical Review E, 69(2), feb 2004. doi10.1103/physreve.69.026113. url https://doi.org/10.1103%2Fphysreve.69.026113.

117

bibliography

[96] P. L. Nunez, R. Srinivasan, A. F. Westdorp, R. S. Wijesinghe, D. M.Tucker, R. B. Silberstein, and P. J. Cadusch. EEG coherency I:Statistics, reference electrode, volume conduction, Laplacians,cortical imaging, and interpretation at multiple scales. Elec-troencephalography and Clinical Neurophysiology, 103(5):499–515,1997. doi 10.1016/S0013-4694(97)00066-7.

[97] S. Ogawa, D. W. Tank, R. Menon, J. M. Ellermann, S. G. Kim,H. Merkle, and K. Ugurbil. Intrinsic signal changes accompa-nying sensory stimulation: functional brain mapping with mag-netic resonance imaging. Proceedings of the National Academy ofSciences, 89(13):5951–5955, jul 1992. doi 10.1073/pnas.89.13.5951.url https://doi.org/10.1073%2Fpnas.89.13.5951.

[98] R. Oostenveld and P. Praamstra. The �ve percent elec-trode system for high-resolution EEG and ERP measurements.Clinical Neurophysiology, 112(4):713–719, 2001. doi 10.1016/S1388-2457(00)00527-7.

[99] G. A. Pavlopoulos, A. L. Wegener, and R. Schneider. A surveyof visualization tools for biological network analysis. BioDataMining, 1(1), nov 2008. doi 10.1186/1756-0381-1-12. url https://doi.org/10.1186%2F1756-0381-1-12.

[100] H. P�ster, V. Kaynig, C. P. Botha, S. Bruckner, V. J. Dercksen, H. C.Hege, and J. B. T. M. Roerdink. Visualization in connectomics.In Mathematics and Visualization, pages 221–245. Springer Lon-don, 2014. doi 10.1007/978-1-4471-6497-5_21. url https://doi.org/10.1007%2F978-1-4471-6497-5_21.

[101] D. Phan, L. Xiao, R. B. Yeh, P. Hanrahan, and T. Winograd.Flow map layout. In IEEE Symposium on Information Visual-ization (InfoVis 2005), 23-25 October 2005, Minneapolis, MN, USA,page 29, 2005. doi 10.1109/INFOVIS.2005.13. url http://doi.ieeecomputersociety.org/10.1109/INFOVIS.2005.13.

[102] T. W. Picton. The p300 wave of the human event-related potential.Journal of Clinical Neurophysiology, 9(4):456–479, Oct 1992. doi10.1097/00004691-199210000-00002. url https://doi.org/10.1097%2F00004691-199210000-00002.

[103] P. E. Rauber, A. X. Falcão, and A. C. Telea. Visualizing time-dependent data using dynamic t-sne. In Proceedings of the Eu-rographics / IEEE VGTC Conference on Visualization: Short Papers,EuroVis ’16, pages 73–77, Goslar Germany, Germany, 2016. Eu-rographics Association. doi 10.2312/eurovisshort.20161164. urlhttps://doi.org/10.2312/eurovisshort.20161164.

118

bibliography

[104] K. Reda, C. Tantipathananandh, A. Johnson, J. Leigh, andT. Berger-Wolf. Visualizing the evolution of community struc-tures in dynamic social networks. Computer Graphics Forum,30(3):1061–1070, jun 2011. doi 10.1111/j.1467-8659.2011.01955.x. url https://doi.org/10.1111%2Fj.1467-8659.2011.01955.x.

[105] P. Riehmann, M. Han�er, and B. Froehlich. Interactive Sankeydiagrams. In IEEE Symposium on Information Visualiza-tion (InfoVis 2005), 23-25 October 2005, Minneapolis, MN, USA,page 31, 2005. doi 10.1109/INFOVIS.2005.21. url http://doi.ieeecomputersociety.org/10.1109/INFOVIS.2005.21.

[106] G. Robertson, R. Fernandez, D. Fisher, B. Lee, and J. Stasko. E�ec-tiveness of animation in trend visualization. IEEE Transactions onVisualization and Computer Graphics, 14(6):1325–1332, 2008. doi10.1109/TVCG.2008.125.

[107] M. Rubinov and O. Sporns. Complex network measures of brainconnectivity: Uses and interpretations. NeuroImage, 52(3):1059–1069, 2010. doi 10.1016/j.neuroimage.2009.10.003. url http://dx.doi.org/10.1016/j.neuroimage.2009.10.003.

[108] Y. Rubner, C. Tomasi, and L. J. Guibas. A metric for distribu-tions with applications to image databases. Computer Vision,1998. Sixth International Conference on, pages 59–66, 1998. doi10.1109/ICCV.1998.710701.

[109] V. Sakkalis. Review of advanced techniques for the estimationof brain connectivity measured with EEG/MEG. Computers inBiology and Medicine, 41(12):1110–1117, dec 2011. doi 10.1016/j.compbiomed.2011.06.020. url https://doi.org/10.1016%2Fj.compbiomed.2011.06.020.

[110] A. Sallaberry, C. Muelder, and K. L. Ma. Clustering, visualiz-ing, and navigating for large dynamic graphs. In W. Didimoand M. Patrignani, editors, Graph Drawing: 20th InternationalSymposium, GD 2012, Redmond, WA, USA, September 19-21, 2012,Revised Selected Papers, pages 487–498. Springer Berlin Heidel-berg, Berlin, Heidelberg, 2013. isbn 978-3-642-36763-2. doi10.1007/978-3-642-36763-2_43. url http://dx.doi.org/10.1007/978-3-642-36763-2_43.

[111] S. E. Schae�er. Graph clustering. Computer Science Review, 1(1):27–64, 2007. doi 10.1016/j.cosrev.2007.05.001.

[112] J. Sorger, K. Bühler, F. Schulze, T. Liu, and B. Dickson. neu-roMAP - interactive graph-visualization of the fruit �y’sneural circuit. In Biological Data Visualization (BioVis), 2013

119

bibliography

IEEE Symposium on, pages 73–80. IEEE, Oct 2013. urlhttps://www.cg.tuwien.ac.at/research/publications/2013/sorger-2013-neuromap/.

[113] Y. Sun, B. Danila, K. Josić, and K. E. Bassler. Improved com-munity structure detection using a modi�ed �ne-tuning strat-egy. EPL (Europhysics Letters), 86(2):28004, apr 2009. doi10.1209/0295-5075/86/28004. url https://doi.org/10.1209%2F0295-5075%2F86%2F28004.

[114] A. Telea and D. Auber. Code �ows: Visualizing structural evo-lution of source code. Computer Graphics Forum, 27(3):831–838,may 2008. doi 10.1111/j.1467-8659.2008.01214.x. url https://doi.org/10.1111%2Fj.1467-8659.2008.01214.x.

[115] C. Turkay, J. Parulek, N. Reuter, and H. Hauser. Interactive vi-sual analysis of temporal cluster structures. Computer Graph-ics Forum, 30(3):711–720, jun 2011. doi 10.1111/j.1467-8659.2011.01920.x. url https://doi.org/10.1111%2Fj.1467-8659.2011.01920.x.

[116] S. Van Den Elzen, D. Holten, J. Blaas, and J. J. Van Wijk. Re-ducing Snapshots to Points: A Visual Analytics Approach to Dy-namic Network Exploration. IEEE Transactions on Visualizationand Computer Graphics, 22(1):1–10, 2016. doi 10.1109/TVCG.2015.2468078.

[117] L. J. P. Van Der Maaten, E. O. Postma, and H. J. Van Den Herik.Dimensionality Reduction: A Comparative Review. Journalof Machine Learning Research, 10:1–41, 2009. doi 10.1080/13506280444000102.

[118] C. Vehlow, F. Beck, P. Auwärter, and D. Weiskopf. Visualizing theevolution of communities in dynamic graphs. Computer GraphicsForum, 34(1):277–288, 2015. doi 10.1111/cgf.12512. url http://dx.doi.org/10.1111/cgf.12512.

[119] A. Von Stein and J. Sarnthein. Di�erent frequencies for di�erentscales of cortical integration: From local gamma to long range al-pha/theta synchronization. International Journal of Psychophysi-ology, 38(3):301–313, 2000. doi 10.1016/S0167-8760(00)00172-0.

[120] J. Wang, L. Wang, Y. Zang, H. Yang, H. Tang, Q. Gong, Z. Chen,C. Zhu, and Y. He. Parcellation-dependent small-world brainfunctional networks: A resting-state fMRI study. Human BrainMapping, 30(5):1511–1523, may 2009. doi 10.1002/hbm.20623.url https://doi.org/10.1002%2Fhbm.20623.

120

bibliography

[121] M. Wichterich, A. M. Ivanescu, and T. Seidl. Feature-based graphsimilarity with co-occurrence histograms and the earth mover’sdistance. In BTW, pages 135–146, 2011.

[122] K. J. Worsley, J. I. Chen, J. Lerch, and A. C. Evans. Comparingfunctional connectivity via thresholding correlations and singu-lar value decomposition. Philosophical Transactions of the RoyalSociety B: Biological Sciences, 360(1457):913–920, may 2005. doi 10.1098/rstb.2005.1637. url https://doi.org/10.1098%2Frstb.2005.1637.

[123] M. Xia, J. Wang, and Y. He. BrainNet viewer: A network visu-alization tool for human brain connectomics. PLoS ONE, 8(7):e68910, jul 2013. doi 10.1371/journal.pone.0068910. url https://doi.org/10.1371%2Fjournal.pone.0068910.

[124] K. S. Xu, M. Kliger, and A. O. Hero III. Visualizing the Tem-poral Evolution of Dynamic Networks. Proc. 9th Work-shop on Mining and Learning with Graphs, page 2, 2011.url http://web.eecs.umich.edu/~hero/Preprints/Xu_

visualization_MLG_2011.pdf.

[125] K. S. Xu, M. Kliger, and A. O. Hero. A regularized graph lay-out framework for dynamic network visualization. Data Min-ing and Knowledge Discovery, 27(1):84–116, aug 2012. doi10.1007/s10618-012-0286-6. url https://doi.org/10.1007%2Fs10618-012-0286-6.

[126] J. S. Yi, N. Elmqvist, and S. Lee. Timematrix: Analyzing tempo-ral social networks using interactive matrix-based visualizations.International Journal of Human-Computer Interaction, 26(11-12):1031–1051, 2010. doi 10.1080/10447318.2010.516722.

121