AG 8 44

Publikationen

2022
•	C. Cai, L. Hinkley, Y. Gao, A. Hashemi, S. Haufe, K. Sekihara and S. S. Nagarajan Empirical Bayesian localization of event-related time-frequency neural activity dynamics. NeuroImage, 258 119369, 2022. [DOI: 10.1016/j.neuroimage.2022.119369] [https://linkinghub.elsevi...eve/pii/S1053811922004888]
•	N. Langer, M. B. Plomecka, M. Tröndle, A. Negi, T. Popov, M. Milham and S. Haufe A benchmark for prediction of psychiatric multimorbidity from resting EEG data in a large pediatric sample. NeuroImage, 258 119348, 2022. [DOI: 10.1016/j.neuroimage.2022.119348] [https://linkinghub.elsevi...eve/pii/S1053811922004670]
•	R. Wilming, C. Budding, K.-R. Müller and S. Haufe Scrutinizing XAI using linear ground-truth data with suppressor variables. Machine Learning, 111(5), 1903--1923, 2022. [DOI: 10.1007/s10994-022-06167-y] [https://link.springer.com...0.1007/s10994-022-06167-y]
•	D. Kumral, E. Cesnaite, F. Beyer, S. M. Hofmann, T. Hensch, C. Sander, U. Hegerl, S. Haufe, A. Villringer, A. V. Witte and V. V. Nikulin Relationship between regional white matter hyperintensities and alpha oscillations in older adults. Neurobiology of Aging, 112 1--11, 2022. [DOI: 10.1016/j.neurobiolaging.2021.10.006] [https://linkinghub.elsevi...eve/pii/S0197458021003195]
•	T. Merk, V. Peterson, R. Köhler, S. Haufe, R. M. Richardson and W.-J. Neumann Machine learning based brain signal decoding for intelligent adaptive deep brain stimulation. Experimental Neurology, 351 113993, 2022. [DOI: 10.1016/j.expneurol.2022.113993] [https://linkinghub.elsevi...eve/pii/S0014488622000188]
2021
•	L. Oala, A. G. Murchison, P. Balachandran, S. Choudhary, J. Fehr, A. W. Leite, P. G. Goldschmidt, C. Johner, E. D. M. Schörverth, R. Nakasi, M. Meyer, F. Cabitza, P. Baird, C. Prabhu, E. Weicken, X. Liu, M. Wenzel, S. Vogler, D. Akogo, S. Alsalamah, E. Kazim, A. Koshiyama, S. Piechottka, S. Macpherson, I. Shadforth, R. Geierhofer, C. Matek, J. Krois, B. Sanguinetti, M. Arentz, P. Bielik, S. Calderon-Ramirez, A. Abbood, N. Langer, S. Haufe, F. Kherif, S. Pujari, W. Samek and T. Wiegand Machine Learning for Health: Algorithm Auditing & Quality Control. Journal of Medical Systems, 45(12), 105, 2021. [DOI: 10.1007/s10916-021-01783-y] [https://link.springer.com...0.1007/s10916-021-01783-y]
•	A. Hashemi, C. Cai, G. Kutyniok, K.-R. Müller, S. S. Nagarajan and S. Haufe Unification of sparse Bayesian learning algorithms for electromagnetic brain imaging with the majorization minimization framework. NeuroImage, 239 118309, 2021. [DOI: 10.1016/j.neuroimage.2021.118309] [https://linkinghub.elsevi...eve/pii/S1053811921005851]
•	C. Cai, A. Hashemi, M. Diwakar, S. Haufe, K. Sekihara and S. S. Nagarajan Robust estimation of noise for electromagnetic brain imaging with the champagne algorithm. NeuroImage, 225 117411, 2021. [DOI: 10.1016/j.neuroimage.2020.117411] [https://linkinghub.elsevi...eve/pii/S105381192030896X]
•	G. Lichtner, F. Balzer, S. Haufe, N. Giesa, F. Schiefenhövel, M. Schmieding, C. Jurth, W. Kopp, A. Akalin, S. J. Schaller, S. Weber-Carstens, C. Spies and F. von Dincklage Predicting lethal courses in critically ill COVID-19 patients using a machine learning model trained on patients with non-COVID-19 viral pneumonia. Scientific Reports, 11(1), 13205, 2021. [DOI: 10.1038/s41598-021-92475-7] [http://www.nature.com/articles/s41598-021-92475-7]
•	A. Hashemi, Y. Gao, C. Cai, S. Ghosh, K.-R. M\"uller, S. Nagarajan and S. Haufe Efficient hierarchical Bayesian inference for spatio-temporal regression models in neuroimaging. In M. Ranzato and A. Beygelzimer and Y. Dauphin and P.S. Liang and J. Wortman Vaughan, Editor, Advances in Neural Information Processing Systems Band 34 , Seite 24855--24870 Herausgeber: Curran Associates, Inc., 2021. [https://proceedings.neuri...6c4d55e0bb067c8-Paper.pdf]

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