Machine learning for BCI: Towards analysing cognition

Klaus Muller

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This abstract will talk about 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 abstracts. Due to the review character of the presentation a high overlap to 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).

Original languageEnglish
Title of host publication4th International Winter Conference on Brain-Computer Interface, BCI 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781467378413
DOIs
Publication statusPublished - 2016 Apr 20
Event4th International Winter Conference on Brain-Computer Interface, BCI 2016 - Gangwon Province, Korea, Republic of
Duration: 2016 Feb 222016 Feb 24

Other

Other4th International Winter Conference on Brain-Computer Interface, BCI 2016
CountryKorea, Republic of
CityGangwon Province
Period16/2/2216/2/24

Fingerprint

Learning systems
Neuroimaging
Brain computer interface
Electroencephalography
Image coding
Decoding
Labels
Signal processing
Fusion reactions
Monitoring
Experiments

ASJC Scopus subject areas

  • Human-Computer Interaction

Cite this

Muller, K. (2016). Machine learning for BCI: Towards analysing cognition. In 4th International Winter Conference on Brain-Computer Interface, BCI 2016 [7457453] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IWW-BCI.2016.7457453

Machine learning for BCI : Towards analysing cognition. / Muller, Klaus.

4th International Winter Conference on Brain-Computer Interface, BCI 2016. Institute of Electrical and Electronics Engineers Inc., 2016. 7457453.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Muller, K 2016, Machine learning for BCI: Towards analysing cognition. in 4th International Winter Conference on Brain-Computer Interface, BCI 2016., 7457453, Institute of Electrical and Electronics Engineers Inc., 4th International Winter Conference on Brain-Computer Interface, BCI 2016, Gangwon Province, Korea, Republic of, 16/2/22. https://doi.org/10.1109/IWW-BCI.2016.7457453
Muller K. Machine learning for BCI: Towards analysing cognition. In 4th International Winter Conference on Brain-Computer Interface, BCI 2016. Institute of Electrical and Electronics Engineers Inc. 2016. 7457453 https://doi.org/10.1109/IWW-BCI.2016.7457453
Muller, Klaus. / Machine learning for BCI : Towards analysing cognition. 4th International Winter Conference on Brain-Computer Interface, BCI 2016. Institute of Electrical and Electronics Engineers Inc., 2016.
@inproceedings{14515258d46a4cb8905fcd32ceec9563,
title = "Machine learning for BCI: Towards analysing cognition",
abstract = "This abstract will talk about 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 abstracts. Due to the review character of the presentation a high overlap to 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, M{\"u}ller-Putz et al. 2015, D{\"a}hne 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, D{\"a}hne 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 M{\"u}ller et al. 2008, B{\"u}nau et al. 2009, Tomioka and M{\"u}ller, 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).",
author = "Klaus Muller",
year = "2016",
month = "4",
day = "20",
doi = "10.1109/IWW-BCI.2016.7457453",
language = "English",
isbn = "9781467378413",
booktitle = "4th International Winter Conference on Brain-Computer Interface, BCI 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - GEN

T1 - Machine learning for BCI

T2 - Towards analysing cognition

AU - Muller, Klaus

PY - 2016/4/20

Y1 - 2016/4/20

N2 - This abstract will talk about 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 abstracts. Due to the review character of the presentation a high overlap to 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).

AB - This abstract will talk about 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 abstracts. Due to the review character of the presentation a high overlap to 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).

UR - http://www.scopus.com/inward/record.url?scp=84969244165&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84969244165&partnerID=8YFLogxK

U2 - 10.1109/IWW-BCI.2016.7457453

DO - 10.1109/IWW-BCI.2016.7457453

M3 - Conference contribution

AN - SCOPUS:84969244165

SN - 9781467378413

BT - 4th International Winter Conference on Brain-Computer Interface, BCI 2016

PB - Institute of Electrical and Electronics Engineers Inc.

ER -