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2024

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M. Oliveira, R. Wilming, B. Clark, C. Budding, F. Eitel, K. Ritter and S. Haufe
Benchmarking the influence of pre-training on explanation performance in MR image classification.
Frontiers in Artificial Intelligence, 7
2024.
[DOI: 10.3389/frai.2024.1330919]
[https://www.frontiersin.o...10.3389/frai.2024.1330919]

2023

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R. Wilming, L. Kieslich, B. Clark and S. Haufe
Theoretical Behavior of XAI Methods in the Presence of Suppressor Variables.
In Krause, Andreas and Brunskill, Emma and Cho, Kyunghyun and Engelhardt, Barbara and Sabato, Sivan and Scarlett, Jonathan, Editor, Proceedings of the 40th International Conference on Machine Learning Band 202 aus Proceedings of Machine Learning Research , Seite 37091--37107
Herausgeber: PMLR,
2023.
[https://proceedings.mlr.press/v202/wilming23a.html]
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E. Cesnaite, P. Steinfath, M. Jamshidi Idaji, T. Stephani, D. Kumral, S. Haufe, C. Sander, T. Hensch, U. Hegerl, S. Riedel-Heller, S. Röhr, M. L. Schroeter, A. Witte, A. Villringer and V. V. Nikulin
Alterations in rhythmic and non‐rhythmic resting‐state EEG activity and their link to cognition in older age.
NeuroImage, 268
119810,
2023.
[DOI: 10.1016/j.neuroimage.2022.119810]
[https://linkinghub.elsevi...eve/pii/S1053811922009314]
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F. Pellegrini, A. Delorme, V. Nikulin and S. Haufe
Identifying good practices for detecting inter-regional linear functional connectivity from EEG.
NeuroImage, 277
120218,
2023.
[DOI: 10.1016/j.neuroimage.2023.120218]
[https://linkinghub.elsevi...eve/pii/S1053811923003695]
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C. Cai, H. Kang, A. Hashemi, D. Chen, M. Diwakar, S. Haufe, K. Sekihara, W. Wu and S. S. Nagarajan
Bayesian Algorithms for Joint Estimation of Brain Activity and Noise in Electromagnetic Imaging.
IEEE Transactions on Medical Imaging, 42(3),
762--773,
2023.
[DOI: 10.1109/TMI.2022.3218074]
[https://ieeexplore.ieee.org/document/9932439/]
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S. Haufe, I. U. Isaias, F. Pellegrini and C. Palmisano
Gait Event Prediction Using Surface Electromyography in Parkinsonian Patients.
Bioengineering, 10(2),
212,
2023.
[DOI: 10.3390/bioengineering10020212]
[https://www.mdpi.com/2306-5354/10/2/212]
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C. Cai, Y. Long, S. Ghosh, A. Hashemi, Y. Gao, M. Diwakar, S. Haufe, K. Sekihara, W. Wu and S. S. Nagarajan
Bayesian adaptive beamformer for robust electromagnetic brain imaging of correlated sources in high spatial resolution.
IEEE Transactions on Medical Imaging,
1--1,
2023.
[DOI: 10.1109/TMI.2023.3256963]
[https://ieeexplore.ieee.org/document/10068553/]
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J. Del Vecchio Del Vecchio, I. Hanafi, N. G. Pozzi, P. Capetian, I. U. Isaias, S. Haufe and C. Palmisano
Pallidal Recordings in Chronically Implanted Dystonic Patients: Mitigation of Tremor-Related Artifacts.
Bioengineering, 10(4),
476,
2023.
[DOI: 10.3390/bioengineering10040476]
[https://www.mdpi.com/2306-5354/10/4/476]

2022

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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]
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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]
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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]
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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]
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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]
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A. Hashemi, C. Cai, Y. Gao, S. Ghosh, K.-R. Müller, S. S. Nagarajan and S. Haufe
Joint Learning of Full-structure Noise in Hierarchical Bayesian Regression Models.
IEEE Transactions on Medical Imaging,
1--1,
2022.
[DOI: 10.1109/TMI.2022.3224085]
[https://ieeexplore.ieee.org/document/9963991/]
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F. Pellegrini, V. Nikulin and S. Haufe
P 80 How to avoid measurement of spurious inter-regional functional connectivity from EEG – a simulation study.
Clinical Neurophysiology, 137
e60--e61,
2022.
[DOI: 10.1016/j.clinph.2022.01.111]
[https://linkinghub.elsevi...eve/pii/S1388245722001298]
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Erratum: Stephani et al., “Temporal Signatures of Criticality in Human Cortical Excitability as Probed by Early Somatosensory Responses”.
The Journal of Neuroscience, 42(22),
4605--4605,
2022.
[DOI: 10.1523/JNEUROSCI.0820-22.2022]
[https://www.jneurosci.org...23/JNEUROSCI.0820-22.2022]
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C. Palmisano, L. Beccaria, S. Haufe, J. Volkmann, G. Pezzoli and I. U. Isaias
Gait Initiation Impairment in Patients with Parkinson’s Disease and Freezing of Gait.
Bioengineering, 9(11),
639,
2022.
[DOI: 10.3390/bioengineering9110639]
[https://www.mdpi.com/2306-5354/9/11/639]

2021

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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]
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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]
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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]
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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]
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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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