Machine learning for BCI: towards analysing cognition

Abstract

This article discusses machine learning and BCI with a focus on analysing cognition, a topic that has been extensively covered by the author and co-workers in numerous papers and conference papers. Due to the review character of the presentation, a high overlap with the above-mentioned contributions is unavoidable. When analysing cognition, it is often useful to combine information from various modalities (see e.g. Biessmann et al., 2011, 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) or as data analysis technique for extracting (non-) linear 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). Note that fusing information has also been a very common practice in the sciences and engineering (Waltz and Llinas, 1990). 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. I will introduce 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). It is applied to estimate and analyse cognitive properties in neurophysiological data from BCI experiments (Samek et al. 2016). Furthermore if time permits I will discuss a recent attractive application of BCI in the context of video coding (Scholler et al. 2012 and Acqualagna et al 2015).