Brain-computer interfaces (BCIs) translate neural recordings into signals that may be used for communication and/or the control of neuroprosthetic devices. Research in this domain poses interesting challenges to machine learning, because data is typically scarce, noisy, and non-stationary [ ]. Furthermore, good decoding algorithms are contingent on domain knowledge that is not readily available and difficult to incorporate into traditional statistical methods. Accordingly, we employ machine-learning methods to study neural processes involved in BCI-control and use these insights to develop novel decoding algorithms and enhance experimental paradigms.
\paragraph{Machine learning algorithms for brain-state decoding}
A crucial aspect of our work is the development of algorithms for real-time brain-state decoding. Building upon our experience in Bayesian inference, we have developed a graphical model decoding framework for ERP-based visual speller systems [ ]. This framework incorporates prior information on letter frequencies into the decoding process, thereby enhancing decoding performance. Furthermore, we were the first to successfully apply the framework of multi-task learning to the domain of BCIs [ ]. As the signal characteristics used by subjects to control a BCI share common aspects, the incorporation of data from previously recorded subjects substantially decreases calibration time and enhances overall decoding performance [ ]. Besides working on methods for real-time decoding, we also develop tools to investigate the neural basis of disorders of cognition. This is essential to understand how the diseased brain differs from the one of healthy subjects, which has implications for the design of BCI systems for patient populations. Building upon the framework of Causal Bayesian Networks, we were the first to provide a comprehensive set of causal interpretation rules for neuroimaging studies [ ]. Extending this line of work, we have developed a causal inference method that is able to detect causal relations between two brain processes, even in the presence of latent confounders [ ].
\paragraph{Brain-computer interfaces for communication}
Building upon our machine-learning methods, we have investigated the neural basis of the ability to operate a BCI in healthy subjects and in patient populations. We could show that the configuration of large-scale cortical networks, as represented in high-frequency gamma-oscillations of the brain's electromagnetic field, influences a subject's ability to communicate with a BCI [ ]. Building upon these insights, we have developed a novel class of BCIs for patients in late stages of amyotrophic lateral sclerosis [ ].
\paragraph{Brain-computer interfaces for rehabilitation}
While BCIs were initially conceived as communication devices for the severely disabled, we have argued that they can also be used for stroke rehabilitation [ ]. By combining a BCI with a seven degrees-of-freedom robotic arm, that serves an exoskeleton, we could show a brain-controlled rehabilitation robot supports patient with chronic stroke in self-regulation of sensorimotor brain rhythms [ ]. The concept of brain-controlled rehabilitation robotics can be extended to systems that monitor patients' learning progress adapt the rehabilitation exercise in real-time [ ].
Jayaram, V., Alamgir, M., Altun, Y., Schölkopf, B., Grosse-Wentrup, M.
Transfer Learning in Brain-Computer Interfaces
IEEE Computational Intelligence Magazine, 11(1):20-31, 2016 (article)
Grosse-Wentrup, M., Janzing, D., Siegel, M., Schölkopf, B.
Identification of causal relations in neuroimaging data with latent confounders: An instrumental variable approach
NeuroImage, 125, pages: 825-833, 2016 (article)
Hohmann, M. R., Fomina, T., Jayaram, V., Widmann, N., Förster, C., Müller vom Hagen, J., Synofzik, M., Schölkopf, B., Schöls, L., Grosse-Wentrup, M.
A Cognitive Brain-Computer Interface for Patients with Amyotrophic Lateral Sclerosis
In Proceedings of the 2015 IEEE International Conference on Systems, Man, and Cybernetics, pages: 3187-3191, SMC, 2015 (inproceedings)
Weichwald, S., Meyer, T., Özdenizci, O., Schölkopf, B., Ball, T., Grosse-Wentrup, M.
Causal interpretation rules for encoding and decoding models in neuroimaging
NeuroImage, 110, pages: 48–59, 2015 (article)
Grosse-Wentrup, M., Schölkopf, B.
A Brain-Computer Interface Based on Self-Regulation of Gamma-Oscillations in the Superior Parietal Cortex
Journal of Neural Engineering, 11(5):056015, 2014 (article)
Meyer, T., Peters, J., Zander, T., Schölkopf, B., Grosse-Wentrup, M.
Predicting Motor Learning Performance from Electroencephalographic Data
Journal of NeuroEngineering and Rehabilitation, 11:24, 2014 (article)
Grosse-Wentrup, M., Schölkopf, B.
High gamma-power predicts performance in sensorimotor-rhythm brain-computer interfaces
Journal of Neural Engineering, 9(4):046001, May 2012 (article)
Hill, N., Schölkopf, B.
An online brain–computer interface based on shifting attention to concurrent streams of auditory stimuli
Journal of Neural Engineering, 9(2):026011, February 2012 (article)
Grosse-Wentrup, M., Mattia, D., Oweiss, K.
Using brain–computer interfaces to induce neural plasticity and restore function
Journal of Neural Engineering, 8(2):1-5, April 2011 (article)
Grosse-Wentrup, M., Schölkopf, B., Hill, J.
Causal Influence of Gamma Oscillations on the Sensorimotor Rhythm
NeuroImage, 56(2):837-842, May 2011 (article)
Krusienski, D., Grosse-Wentrup, M., Galan, F., Coyle, D., Miller, K., Forney, E., Anderson, C.
Critical issues in state-of-the-art brain–computer interface signal processing
Journal of Neural Engineering, 8(2):1-8, April 2011 (article)
Martens, S., Mooij, J., Hill, N., Farquhar, J., Schölkopf, B.
A graphical model framework for decoding in the visual ERP-based BCI speller
Neural Computation, 23(1):160-182, January 2011 (article)
Gomez Rodriguez, M., Peters, J., Hill, J., Schölkopf, B., Gharabaghi, A., Grosse-Wentrup, M.
Closing the sensorimotor loop: haptic feedback facilitates decoding of motor imagery
Journal of Neural Engineering, 8(3):1-12, June 2011 (article)
Barbero, A., Grosse-Wentrup, M.
Biased Feedback in Brain-Computer Interfaces
Journal of NeuroEngineering and Rehabilitation, 7(34):1-4, July 2010 (article)
Gomez Rodriguez, M., Peters, J., Hill, J., Schölkopf, B., Gharabaghi, A., Grosse-Wentrup, M.
Closing the sensorimotor loop: Haptic feedback facilitates decoding of arm movement imagery
In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC 2010), pages: 121-126, IEEE, Piscataway, NJ, USA, IEEE International Conference on Systems, Man and Cybernetics (SMC), October 2010 (inproceedings)
Alamgir, M., Grosse-Wentrup, M., Altun, Y.
Multitask Learning for Brain-Computer Interfaces
In JMLR Workshop and Conference Proceedings Volume 9: AISTATS 2010, pages: 17-24, (Editors: Teh, Y.W. , M. Titterington), JMLR, Cambridge, MA, USA, Thirteenth International Conference on Artificial Intelligence and Statistics , May 2010 (inproceedings)
Grosse-Wentrup, M., Liefhold, C., Gramann, K., Buss, M.
Beamforming in Noninvasive Brain-Computer Interfaces
IEEE Transactions on Biomedical Engineering, 56(4):1209-1219, April 2009 (article)
Martens, S., Hill, N., Farquhar, J., Schölkopf, B.
Overlap and refractory effects in a Brain-Computer Interface speller based on the visual P300 Event-Related Potential
Journal of Neural Engineering, 6(2):1-9, April 2009 (article)