#ChaireD3S – La chaire «Data Sciences for Social sciences » co-organise le semestre thématique «Non-stationnarité, cyclo-stationnarité et applications »

Heure locale

  • Fuseau horaire : America/New_York
  • Date : 24 - 31 Jan 2023

Date

24 - 31 Jan 2023
Expiré!
QR Code

L'événement est terminé.

La chaire «Data Sciences for Social Sciences - D3S » co-organise le semestre thématique sur "Non-stationnarité, cyclo-stationnarité et applications".

📣 EN RAISON  DE LA GRÉVE, LE DERNIER COURS DE HERNANDO OMBAO SUR LA  “STATISTICS OF EGG” AYANT LIEU MARDI 31 JANVIER DE 10h A 12h SE TIENDRA EN DISTANCIEL : https://kaust.zoom.us/j/91429637260

La chaire «Data Sciences for Social Sciences – D3S » en partenariat avec les laboratoires MODAL’X (Université Paris Nanterre et LABEX MME-DII (Fondation Paris Cercy Université), organise de janvier à juin 2023 un semestre thématique sur “Non-stationnarité, cyclo-stationnarité et applications”. 

Ce 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.

Le 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.

Le programme des séances est le suivant:


24 January 2023 13h – 15h

  • Design of Neuroscience Experiments
  • Introduction to EEGs and fMRI
  • EEG Signal Artifacts: principles, simulations and removal

25 January 2023 10h – 12h

  • Simulating EEGs
  • Bandpass Filtering
  • Introduction to Spectral Analysis

26 January 2023 13h30 – 15h30

  • Spectral Estimation
    Connectivity Analysis
  • 27 January 2023 14h – 16h
  • Overview of Statistical Inference
  • Regression Models
  • Statistical Models for Group Analysis

31 January 2023 10h – 12h

  • Models for comparing experimental conditions and populations
  • Fitting causality models


Pour les extérieurs qui souhaitent déjeuner sur place avant ou après les séances merci de prévenir à l’avance (contact : patrice.bertail@parisnanterre.fr)
.

Pour ceux qui ne pourraient pas se déplacer, voici le lien zoom qui sera utilisé pendant tous les cours

Les videos seront mises ensuite dans un répertoire de  dropbox.

La 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.

REFERENCES

General content

Ombao, H., Lindquist, M., Thompson, W., & Aston, J. (2017). Handbook of neuroimaging data analysis.

EEG simulation

Granados-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

Multivariate 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

Gao, 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

Shumway, R. H., & Stoffer, D. S. (2006). Time series analysis and its applications: With R examples (2nd ed). Springer.

Artifact identification and removal

Fatt, I., & Weissman, B. A. (2013). Physiology of the eye: an introduction to the vegetative functions. Butterworth-Heinemann

Shams, 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

Delorme, 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

Sweeney, 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

Spectral analysis

Ombao, H., & Pinto, M. (2022). Spectral Dependence. Econometrics and Statistics. https://doi.org/10.1016/j.ecosta.2022.10.005

Ombao, 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

Fiecas, 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

Ombao, H., & Bellegem, S. V. (2006). Coherence Analysis of Nonstationary Time Series: A Linear Filtering Point of View. 24.

Connectivity models

Ting, 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

Degras, 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

Bourakna, 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

Pinto-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

Dutta, 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

Phang, 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

Ting, 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

Samdin, 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

Wang, 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

Retour haut de page