Klaus-Robert Müller

Multivariate Machine Learning Methods for Fusing Multimodal Functional Neuroimaging Data

Multimodal data are ubiquitous in engineering, communications, robotics, computer vision, or more generally speaking in industry and the sciences. All disciplines have developed their respective sets of analytic tools to fuse the information that is available in all measured modalities. In this paper, we provide a review of classical as well as recent machine learning methods (specifically factor models) for fusing information from functional neuroimaging techniques such as: LFP, EEG, MEG, fNIRS, and fMRI. Early and late fusion scenarios are distinguished, and appropriate factor models for the respective scenarios are presented along with example applications from selected multimodal neuroimaging studies. Further emphasis is given to the interpretability of the resulting model parameters, in particular by highlighting how factor models relate to physical models needed for source localization. The methods we discuss allow for the extraction of information from neural data, which ultimately contributes to 1) better neuroscientific understanding; 2) enhance diagnostic performance; and 3) discover neural signals of interest that correlate maximally with a given cognitive paradigm. While we clearly study the multimodal functional neuroimaging challenge, the discussed machine learning techniques have a wide applicability, i.e., in general data fusion, and may thus be informative to the general interested reader.

Robust common spatial patterns based on Bhattacharyya distance and Gamma divergence

The computation of task-related spatial filters is a prerequisite for a successful application of motor imagery-based Brain-Computer Interfaces (BCI). However, in the presence of artifacts, e.g., resulting from eye movements or muscular activity, standard methods such as Common Spatial Patterns (CSP) perform poorly. Recently, a divergence-based spatial filter computation framework has been proposed which enables significantly more robust computation with respect to artifacts by using Beta divergence. In this paper we integrate two additional divergence measures, namely Bhattacharyya distance and Gamma divergence, into the divergence-based CSP framework and evaluate their robustness using simulations and data set IVa from BCI Competition III.

Welcome message from the general chairs

It is again a great pleasure to welcome you to the 5th International Winter Conference on Brain-Computer Interface in High1 resort. This is the fifth event of the annual Brain-Computer Interface seasonal conference and it has been a great successful tradition so far.

Multifrequency Analysis of Brain-Computer Interfaces

Modern brain computer interfaces (BCI) rely on an extensive use of machine learning and signal processing techniques. This review will focus on an important prerequisite, namely spectral preprocessing. In particular, the optimal usage of multiple frequency features for BCI is discussed in general along with the commonly employed tricks for frequency choice. This is linked to the underlying physiology. Finally, applications of the multifrequency framework are given: (a) to BCI in general and (b) for analysing the BCI illiterates phenomenon.