English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Multimodal integration of electrophysiological and hemodynamic signals

Dähne, S., Bießmann, F., Meinecke, F. C., Mehnert, J., Fazli, S., & Müller, K. R. (2014). Multimodal integration of electrophysiological and hemodynamic signals. In 2014 International Winter Workshop on Brain-Computer Interface (BCI). Piscataway, NJ: IEEE. doi:10.1109/iww-BCI.2014.6782552.

Item is

Files

show Files

Locators

show

Creators

show
hide
 Creators:
Dähne, Sven1, Author
Bießmann, Felix2, Author
Meinecke, Frank C.1, Author
Mehnert, Jan3, Author              
Fazli, Siamac1, 2, Author
Müller, Klaus Robert2, Author
Affiliations:
1Department of Machine Learning, TU Berlin, Germany, ou_persistent22              
2Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea, ou_persistent22              
3Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              

Content

show
hide
Free keywords: -
 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).

Details

show
hide
Language(s): eng - English
 Dates: 2014-02
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1109/iww-BCI.2014.6782552
 Degree: -

Event

show
hide
Title: 2014 International Winter Workshop on Brain-Computer Interface (BCI)
Place of Event: Jeongsun-kun, Republic of Korea
Start-/End Date: 2014-02-17 - 2014-02-19

Legal Case

show

Project information

show

Source 1

show
hide
Title: 2014 International Winter Workshop on Brain-Computer Interface (BCI)
Source Genre: Proceedings
 Creator(s):
Affiliations:
Publ. Info: Piscataway, NJ : IEEE
Pages: 4 Volume / Issue: - Sequence Number: - Start / End Page: - Identifier: ISBN: 978-1-479-92589-6