Multimodal and Multivariate Machine Learning Methods for Nonlinearly Coupled Oscillatory Systems
Human Cognitive and Systems Neuroscience
Software Engineering and Programming Languages
Theoretical Computer Science
Final Report Abstract
In the course of this project we have contributed a set of decomposition (or factor-) methods, specifically designed for the extraction of amplitude modulated oscillatory components from highdimensional uni- and multimodal neuroimaging recordings. We have studied the strengths and weaknesses of the proposed methods using theoretical considerations as well as numerical simulations. Additionally, we have demonstrated the practical utility of our methods in a multitude of different real-world neuroimaging datasets recorded with a total of four measurement modalities (EEG, MEG, fNIRS, and fMRI). The results obtained on the real-world datasets have been in line with what was expected beforehand or with findings confirmed by the literature which demonstrates the validity of our methodological developments. Ongoing and Future Work. The presented compelling evidence for the correctness of our approach, makes it an attractive choice for future studies of amplitude modulated neural sources, their relation to other aspects of brain activity in within-subject analysis or to corresponding activity from other brains in across-subject analysis, studied in academical, commercial, or clinical settings. In particular, it is currently applied to new paradigms in cognitive and auditory neuroscience. Concerning the future work of extending the SPoC approach, we are investigating multivariate target functions further. In applications this allows for multimodal user feedback. On top of that, we would like to explore spiky noise elements within correlated components or sensor data.
Publications
- “Finding brain oscillations with power dependencies in neuroimaging data,” NeuroImage, vol. 96, pp. 334–348, 2014
S. Dähne, V. V. Nikulin, D. Ramírez, P. J. Schreier, K.-R. Müller, and S. Haufe
(See online at https://doi.org/10.1016/j.neuroimage.2014.03.075) - “Spoc: A novel framework for relating the amplitude of neuronal oscillations to behaviorally relevant parameters,” NeuroImage, vol. 86, pp. 111–122, 2014
S. Dähne, F. C. Meinecke, S. Haufe, J. Höhne, M. Tangermann, K.-R. Müller, and V. V. Nikulin
(See online at https://doi.org/10.1016/j.neuroimage.2013.07.079) - “Identifying granger causal relationships between neural power dynamics and variables of interest,” NeuroImage, vol. 111, pp. 489–504, 2015
I. Winkler, S. Haufe, A. K. Porbadnigk, K.-R. Müller, and S. Dähne
(See online at https://doi.org/10.1016/j.neuroimage.2014.12.059) - “Multivariate machine learning methods for fusing multimodal functional neuroimaging data,” Proceedings of the IEEE, vol. 103, no. 9, pp. 1507–1530, 2015
S. Dähne, F. Bießmann, W. Samek, S. Haufe, D. Goltz, C. Gundlach, A. Villringer, S. Fazli, and K.-R. Mü ller
(See online at https://doi.org/10.1109/JPROC.2015.2425807) - “The berlin brain-computer interface: Progress beyond communication and control,” Frontiers in neuroscience, vol. 10, 2016
B. Blankertz, L. Acqualagna, S. Dähne, S. Haufe, M. Schultze-Kraft, I. Sturm, M. Ušćmlic, M. A. Wenzel, G. Curio, and K.-R. Mü ller
(See online at https://doi.org/10.3389/fnins.2016.00530) - “Unsupervised classification of operator workload from brain signals,” Journal of neural engineering, vol. 13, no. 3, p. 036 008, 2016
M. Schultze-Kraft, S. Dähne, M. Gugler, G. Curio, and B. Blankertz
(See online at https://doi.org/10.1088/1741-2560/13/3/036008)
