BEGIN:VCALENDAR
VERSION:2.0
METHOD:PUBLISH
CALSCALE:GREGORIAN
PRODID:-//WordPress - MECv7.32.0//EN
X-ORIGINAL-URL:https://fondationupn.fr/
X-WR-CALNAME:FUPN.fr
X-WR-CALDESC:Fondation Université Paris Nanterre
X-WR-TIMEZONE:Europe/Paris
BEGIN:VTIMEZONE
TZID:Europe/Paris
X-LIC-LOCATION:Europe/Paris
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20260329T030000
RRULE:FREQ=YEARLY;BYMONTH=03;BYDAY=-1SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20261025T020000
RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=4SU
END:STANDARD
END:VTIMEZONE
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-PUBLISHED-TTL:PT1H
X-MS-OLK-FORCEINSPECTOROPEN:TRUE
BEGIN:VEVENT
CLASS:PUBLIC
UID:MEC-600a4d6403d3914e1e24b13808bd4bd4@fondationupn.fr
DTSTART;TZID=Europe/Paris:20230124T080000
DTEND;TZID=Europe/Paris:20230131T180000
DTSTAMP:20230116T103505Z
CREATED:20230116
LAST-MODIFIED:20230702
PRIORITY:5
SEQUENCE:2
TRANSP:OPAQUE
SUMMARY:[Chaire D3S] Dans le cadre du semestre thématique «Non-stationnarité, cyclo-stationnarité et applications » la chaire «Data Sciences for Social sciences » co-organise l’intervention du professeur Hernando Ombao
DESCRIPTION:📅 du 24 au 31 janvier 2023\n📍SEMESTRE THÉMATIQUE « NON-STATIONNARITÉ , CYCLO-STATIONNARITÉ ET APPLICATIONS » : INTERVENTION DU PROFESSEUR Hernando OMBAO\nLa chaire «Data Sciences for Social Sciences – D3S ( https://fondationupn.fr/data-science/ ) » en partenariat avec les laboratoires MODAL’X ( https://modalx.parisnanterre.fr/ ) (Université Paris Nanterre et LABEX MME-DII ( https://fondation.cyu.fr/fondation-pour-la-modelisation-en-economie-labex-mme-dii/ ) (Fondation Paris Cercy Université), organise de janvier à juin 2023 un semestre thématique sur “Non-stationnarité, cyclo-stationnarité et applications”. \nCe semestre s’organise autour de plusieurs cours thématiques, d’invitations à des séminaires et d’une conférence qui a lieu du 5 au 7 juin.\nLe premier cours de l’édition 2023 du semestre thématique est porté par Hernando Ombao (King Abdullah University of Science and Technology, Saudi Arabia), dont les interventions porteront sur le thème Statistical Analysis of Brain signals.\n \nLe programme des séances est le suivant:24 January 2023 13h – 15h\n\nDesign of Neuroscience Experiments\nIntroduction to EEGs and fMRI\nEEG Signal Artifacts: principles, simulations and removal\n\n25 January 2023 10h – 12h\n\nSimulating EEGs\nBandpass Filtering\nIntroduction to Spectral Analysis\n\n26 January 2023 13h30 – 15h30\n\nSpectral Estimation Connectivity Analysis\n27 January 2023 14h – 16h\nOverview of Statistical Inference\nRegression Models\nStatistical Models for Group Analysis\n\n31 January 2023 10h – 12h\n\nModels for comparing experimental conditions and populations\nFitting causality models\n\nPour les extérieurs qui souhaitent déjeuner sur place avant ou après les séances merci de prévenir à l’avance (contact : patrice.bertail@parisnanterre.fr).\nPour ceux qui ne pourraient pas se déplacer, voici le lien zoom qui sera utilisé pendant tous les cours\nLes videos seront mises ensuite dans un répertoire de  dropbox.\nLa conférence internationale qui clôturera le semestre, “Non-stationarity, cyclo-stationarity and applications” aura lieu au bâtiment Max Weber, Université Paris Nanterre du 5 au 7 juin 2023.\n																\n															\n																\n															\nREFERENCES\nGeneral content\nOmbao, H., Lindquist, M., Thompson, W., & Aston, J. (2017). Handbook of neuroimaging data analysis.\nEEG simulation\nGranados-Garcia, G., Fiecas, M., Babak, S., Fortin, N. J., & Ombao, H. (2022). Brain waves analysis via a non-parametric Bayesian mixture of autoregressive kernels. Computational Statistics & Data Analysis, 174, 107409. https://doi.org/10.1016/j.csda.2021.107409 ( https://doi.org/10.1016/j.csda.2021.107409 )\nMultivariate spectral analysis of time series. (2019). In W. W. S. Wei, Multivariate Time Series Analysis and Applications (pp. 301–436). John Wiley & Sons, Ltd. https://doi.org/10.1002/9781119502951.ch9 ( https://doi.org/10.1002/9781119502951.ch9 )\nGao, X., Shen, W., Shahbaba, B., Fortin, N., & Ombao, H. (2016). Evolutionary State-Space Model and Its Application to Time-Frequency Analysis of Local Field Potentials. Statistica Sinica, 30. https://doi.org/10.5705/ss.202017.0420 ( https://doi.org/10.5705/ss.202017.0420 )\nShumway, R. H., & Stoffer, D. S. (2006). Time series analysis and its applications: With R examples (2nd ed). Springer.\nArtifact identification and removal\nFatt, I., & Weissman, B. A. (2013). Physiology of the eye: an introduction to the vegetative functions. Butterworth-Heinemann\nShams, T., Rahi, F., Mir, M., & Nasor, M. (2009). Home ECG system: Signal processing and remote transmission. 2009 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), 254–258. https://doi.org/10.1109/ISSPIT.2009.5407559 ( https://doi.org/10.1109/ISSPIT.2009.5407559 )\nDelorme, A., Sejnowski, T., & Makeig, S. (2007). Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis. NeuroImage, 34(4), 1443–1449. https://doi.org/10.1016/j.neuroimage.2006.11.004 ( https://doi.org/10.1016/j.neuroimage.2006.11.004 )\nSweeney, K. T., McLoone, S. F., & Ward, T. E. (2013). The use of ensemble empirical mode decomposition with canonical correlation analysis as a novel artifact removal technique. IEEE Transactions on Bio-Medical Engineering, 60(1), 97–105. https://doi.org/10.1109/TBME.2012.2225427 ( https://doi.org/10.1109/TBME.2012.2225427 )\nSpectral analysis\nOmbao, H., & Pinto, M. (2022). Spectral Dependence. Econometrics and Statistics. https://doi.org/10.1016/j.ecosta.2022.10.005 ( https://doi.org/10.1016/j.ecosta.2022.10.005 )\nOmbao, H. (2019). Spectral Approach to Modeling Dependence in Multivariate Time Series. Journal of Physics: Conference Series, 1417, 012007. https://doi.org/10.1088/1742-6596/1417/1/012007 ( https://doi.org/10.1088/1742-6596/1417/1/012007 )\nFiecas, M., & Ombao, H. (2011). The generalized shrinkage estimator for the analysis of functional connectivity of brain signals. The Annals of Applied Statistics, 5(2A), 1102–1125. https://doi.org/10.1214/10-AOAS396 ( https://doi.org/10.1214/10-AOAS396 )\nOmbao, H., & Bellegem, S. V. (2006). Coherence Analysis of Nonstationary Time Series: A Linear Filtering Point of View. 24.\nConnectivity models\nTing, C.-M., Skipper, J. I., Noman, F., Small, S. L., & Ombao, H. (2022). Separating Stimulus-Induced and Background Components of Dynamic Functional Connectivity in Naturalistic fMRI. IEEE Transactions on Medical Imaging, 41(6), 1431–1442. https://doi.org/10.1109/TMI.2021.3139428 ( https://doi.org/10.1109/TMI.2021.3139428 )\nDegras, D., Ting, C.-M., & Ombao, H. (2022). Markov-switching state-space models with applications to neuroimaging. Computational Statistics & Data Analysis, 174, 107525. https://doi.org/10.1016/j.csda.2022.107525 ( https://doi.org/10.1016/j.csda.2022.107525 )\nBourakna, A. E. Y., Pinto, M., Fortin, N., & Ombao, H. (2021). Smooth Online Parameter Estimation for time varying VAR models with application to rat’s LFP data. ArXiv:2102.12290 [Stat]. http://arxiv.org/abs/2102.12290 ( http://arxiv.org/abs/2102.12290 )\nPinto-Orellana, M. A., Mirtaheri, P., Hammer, H. L., & Ombao, H. (2021). SCAU: Modeling spectral causality for multivariate time series with applications to electroencephalograms. ArXiv:2105.06418 [q-Bio, Stat]. http://arxiv.org/abs/2105.06418 ( http://arxiv.org/abs/2105.06418 )\nDutta, C. N., Douglas, P. K., & Ombao, H. (2020). Structural Brain Asymmetries in Youths with Combined and Inattentive Presentations of Attention Deficit Hyperactivity Disorder. ArXiv:2010.13458 [q-Bio]. http://arxiv.org/abs/2010.13458 ( http://arxiv.org/abs/2010.13458 )\nPhang, C.-R., Ting, C.-M., Noman, F., & Ombao, H. (2020). Classification of EEG-Based Brain Connectivity Networks in Schizophrenia Using a Multi-Domain Connectome Convolutional Neural Network. IEEE Journal of Biomedical and Health Informatics, 24(5), 1333–1343. https://doi.org/10.1109/JBHI.2019.2941222 ( https://doi.org/10.1109/JBHI.2019.2941222 )\nTing, C.-M., Ombao, H., Salleh, S.-H., & Latif, A. Z. A. (2020). Multi-Scale Factor Analysis of High-Dimensional Functional Connectivity in Brain Networks. IEEE Transactions on Network Science and Engineering, 7(1), 449–465. https://doi.org/10.1109/TNSE.2018.2869862 ( https://doi.org/10.1109/TNSE.2018.2869862 )\nSamdin, S. B., Ting, C.-M., Ombao, H., & Salleh, S.-H. (2017). A Unified Estimation Framework for State-Related Changes in Effective Brain Connectivity. IEEE Transactions on Biomedical Engineering, 64(4), 844–858. https://doi.org/10.1109/TBME.2016.2580738 ( https://doi.org/10.1109/TBME.2016.2580738 )\nWang, Y., Ting, C.-M., & Ombao, H. (2016). Modeling Effective Connectivity in High-Dimensional Cortical Source Signals. IEEE Journal of Selected Topics in Signal Processing, 10(7), 1315–1325. https://doi.org/10.1109/JSTSP.2016.2600023 ( https://doi.org/10.1109/JSTSP.2016.2600023 )\n
URL:https://fondationupn.fr/events/chaired3s-la-chaire-data-sciences-for-social-sciences-co-organise-le-semestre-thematique-non-stationnarite-cyclo-stationnarite-et-applications_hernando_ombao/
CATEGORIES:Actualité,Chaire D3S
ATTACH;FMTTYPE=image/jpeg:https://fondationupn.fr/wp-content/uploads/2022/07/Data-futur-d3S.jpg
END:VEVENT
END:VCALENDAR