See also my Google Scholar profile.
Machine learning methods and applications for Brain-computer interfaces
Sosulski, J., & Tangermann, M. (2022). Introducing block-Toeplitz covariance matrices to remaster linear discriminant analysis for event-related potential brain-computer interfaces. arXiv Preprint arXiv:2202.02001. Under review.
Meinel, A., Sosulski, J., Schraivogel, S., Reis, J., & Tangermann, M. (2021). Manipulating single-trial motor performance in chronic stroke patients by closed-loop brain state interaction. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 29, 1806–1816.
Sosulski, J., Kemmer, J.-P., & Tangermann, M. (2021). Improving covariance matrices derived from tiny training datasets for the classification of event-related potentials with linear discriminant analysis. Neuroinformatics, 19(3), 461–476.
Sosulski, J., Hübner, D., Klein, A., & Tangermann, M. (2021). Online optimization of stimulation speed in an auditory brain-computer interface under time constraints. arXiv Preprint arXiv:2109.06011. Under review.
Sosulski, J., & Tangermann, M. (2019). Extremely reduced data sets indicate optimal stimulation parameters for classification in brain-computer interfaces. In 2019 41st annual international conference of the IEEE engineering in medicine and biology society (EMBC) (pp. 2256–2260). IEEE.
Sosulski, J., & Tangermann, M. (2019). Spatial filters for auditory evoked potentials transfer between different experimental conditions. In 8th Graz BCI conference.
Ritter, D., & Sosulski, J. (2016). Exception handling in message-based integration systems and modeling using BPMN. International Journal of Cooperative Information Systems, 25(02), 1650004.
Ritter, D., & Sosulski, J. (2014). Modeling exception flows in integration systems. In 2014 IEEE 18th international enterprise distributed object computing conference (pp. 12–21). IEEE.