Multimodal integration of electrophysiological and hemodynamic signals

Sven Dähne, Felix Bießmann, Frank C. Meinecke, Jan Mehnert, Siamac Fazli, Klaus Muller

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

1 Citation (Scopus)

Abstract

The urge to further our understanding of multimodal neural data has recently become an important topic due to the ever increasing availability of simultaneously recorded data from different neural imaging modalities. In case where the electroencephalogram (EEG) is one of the measurement modalities, it is of interest to relate a nonlinear function of the raw EEG time-domain signal, namely the dynamics of EEG bandpower, to another modality such as the hemodynamic response, as measured with near-infrared spectroscopy (NIRS) or functional magnetic resonance imaging (fMRI). In this work we tackle exactly this problem by defining a novel algorithm that we denote multimodal source power correlation analysis (mSPoC). The validity of the mSPoC approach is demonstrated for real-world multimodal data, obtained from a Brain-Computer Interface experiment, where mSPoC's ability to recover common sources from multimodal measurements is contrasted against an existing state-of-art approach represented by canonical correlation analysis (CCA).

Original languageEnglish
Title of host publication2014 International Winter Workshop on Brain-Computer Interface, BCI 2014
PublisherIEEE Computer Society
DOIs
Publication statusPublished - 2014 Jan 1
Event2014 International Winter Workshop on Brain-Computer Interface, BCI 2014 - Gangwon, Korea, Republic of
Duration: 2014 Feb 172014 Feb 19

Other

Other2014 International Winter Workshop on Brain-Computer Interface, BCI 2014
CountryKorea, Republic of
CityGangwon
Period14/2/1714/2/19

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ASJC Scopus subject areas

  • Human-Computer Interaction
  • Human Factors and Ergonomics

Cite this

Dähne, S., Bießmann, F., Meinecke, F. C., Mehnert, J., Fazli, S., & Muller, K. (2014). Multimodal integration of electrophysiological and hemodynamic signals. In 2014 International Winter Workshop on Brain-Computer Interface, BCI 2014 [6782552] IEEE Computer Society. https://doi.org/10.1109/iww-BCI.2014.6782552