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Machine Learning and Brain Computer Interfacing

Klaus R. Mueller1, 2*
  • 1 Technical Univ, Machine Learning Group, Germany
  • 2 Korea Univ, Department of Brain and Cognitive Engineering, Republic of Korea

This abstract will talk about machine learning and BCI with focus about multimodal techniques, a topic that has been extensively covered by the author and co-workers in numerous papers and conference abstracts. Due to the review character of the presentation a high overlap to the above mentioned contributions is unavoidable. A main motivation for multimodal imaging has been the possibility to enhance medical diagnosis (see e.g. Biessmann et al., 2011). Beyond this original medical motivation the fusion of multiple modalities has created successful interesting research opportunities that have furthered our understanding of the brain and cognition (see e.g. Sui et al., 2012). In BCI recently multimodal fusion concepts have received great attention under the label hybrid BCI (Pfurtscheller et al., 2010, Müller-Putz et al. 2015, Dähne et al. 2015, Fazli et al. 2015). Fusing information has also been a very common practice in the sciences and engineering (Waltz and Llinas, 1990). Recently a family of novel multimodal data analysis methods have emerged that can extract nonlinear relations between data (see e.g. Biessmann et al., 2010, Biessmann et al., 2011, Fazli et al., 2009, 2011, 2012, Dähne et al., 2013, 2014a,b, 2015, Winkler et al. 2015). They are rooted in the modern machine learning and signal processing techniques that are now available for analysing EEG, for decoding mental states etc. (see Müller et al. 2008, Bünau et al. 2009, Tomioka and Müller, 2010, Blankertz et al., 2008, 2011, Lemm et al., 2011, Porbadnigk et al. 2015 for recent reviews and contributions to Machine Learning for BCI, see Samek et al. 2014 for a review on robust methods). The talk will discuss a number of recent contributions from the BBCI group that have helped to broaden the spectrum of applicability for Brain Computer Interfaces and mental state monitoring in particular and for analysis of neuroimaging data in general. First I will discuss recent multimodal analysis techniques such as SPoC (Dähne et al., 2013, 2014a,b, 2015, Winkler et al. 2015) and show its application to data simultaneously recorded from EEG and NIRS (Fazli et al. 2012, Fazli et al. 2015). Second, I will introduce a recent experiment where a BCI is used to control an exoskeleton. Here the challenge is that the 7 motors in the exoskeleton produce substantial high amplitude artifacts that essentially prevent reliable control by motor imagery. Therefore our study (Kwak et al. 2015) controls the exoskeleton commands by decoding within an SSVEP BCI paradigm. The latter is highly robust and arrives at high performance across subjects for online and offline experiments. Furthermore if time permits we will discuss a novel reliable method for estimating the Hurst exponent, a quantity that has recently become popular for describing network properties and is being used for diagnostic purposes (cf. Blythe et al. 2014). The mentioned nonlinear techniques allow for a better and more reliable and robust analysis of complex phenomena in neurophysiological data.

Acknowledgements

This abstract is based on joint work with Sven Dähne, Duncan Blythe, Wojciech Samek, Motoaki Kawanabe, Frank Meinecke, Benjamin Blankertz, Gabriel Curio, Michael Tangermann, Carmen Vidaurre, Paul von Bünau, Felix Biessmann, Siamac Fazli and many other members of the Berlin Brain Computer Interface team as well on joint work with No-Sang Kwak and Seong-Whan Lee. We greatly acknowledge funding by BMBF, EU, DFG and NRF.

References

Blankertz, B., Tomioka, R., Lemm, S., Kawanabe, M., & Muller, K. R. (2008). Optimizing spatial filters for robust EEG single-trial analysis. IEEE Signal Processing Magazine, 25(1), 41-56.
Blankertz, B., Lemm, S., Treder, M., Haufe, S., & Müller, K. R. (2011). Single-trial analysis and classification of ERP components—a tutorial. NeuroImage, 56(2), 814-825.
Biessmann, F., Meinecke, F. C., Gretton, A., Rauch, A., Rainer, G., Logothetis, N. K., & Müller, K. R., Temporal kernel CCA and its application in multimodal neuronal data analysis. Machine Learning, 79(1-2), 5-27 (2010)
Biessmann, F. Plis, S., Meinecke, F.C., Eichele, T., Müller, K.-R., Analysis of Neuroimaging Data, IEEE Rev Biomed Eng. 4, 26-58 (2011)
Blankertz, B., Lemm, S., Treder, M., Haufe, S., Müller, K.-R., Single-trial analysis and classifcation of ERP components - A tutorial, Neuroimage, 56 (2), 814-825 (2011)
Blythe, D. A., Haufe, S., Müller, K. R., Nikulin, V. V., The effect of linear mixing in the EEG on Hurst exponent estimation, NeuroImage, 99, 377-387 (2014).
von Bünau, P., Meinecke, F. C., Király, F. C., & Müller, K. R. (2009). Finding stationary subspaces in multivariate time series. Physical Review Letters, 103(21), 214101.
Dähne, S., Biessmann, F., Meinecke F.C., Mehnert, J., Fazli S, Müller, K.-R., Integration of Multivariate Data Streams with Bandpower Signals, IEEE T Multimedia, 15(5), 1001-13 (2013)
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Keywords: Brain computer interface (BCI), machine learning applied to neuroscience, multimodal integration, Multimodal Imaging, exoskeleton

Conference: German-Japanese Adaptive BCI Workshop, Kyoto, Japan, 28 Oct - 29 Oct, 2015.

Presentation Type: Oral presentation (Invited speakers)

Topic: Adaptive BCI

Citation: Mueller KR (2015). Machine Learning and Brain Computer Interfacing. Front. Comput. Neurosci. Conference Abstract: German-Japanese Adaptive BCI Workshop. doi: 10.3389/conf.fncom.2015.56.00006

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Received: 18 Oct 2015; Published Online: 04 Nov 2015.

* Correspondence: Prof. Klaus R Mueller, Technical Univ, Machine Learning Group, Berlin, Germany, Klaus-robert.mueller@tu-berlin.de