@InProceedings{6952003,
  author = {W. Swinnen and B. Hunyadi and E. Acar and S. V. Huffe and M. {De Vos}},
  booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},
  title = {Incorporating higher dimensionality in joint decomposition of EEG and fMRI},
  year = {2014},
  pages = {121-125},
  abstract = {EEG-fMRI research to study brain function became popular because of the complementarity of the modalities. Through the use of data-driven approaches such as jointICA, sources extracted from EEG can be linked to regions in fMRI. Joint-ICA in its standard formulation however does not allow for the inclusion of multiple EEG electrodes, so it is a rather arbitrary choice which electrode is used in the analysis. In this study, we explore several ways to include the higher dimensionality of the EEG during a joint decomposition of EEG and fMRI. Our results show that incorporation of multiple channels in the jointICA can reveal new relations between fMRI activation maps and ERP features.},
  keywords = {bioelectric potentials;biomedical electrodes;biomedical MRI;electroencephalography;feature extraction;medical image processing;neurophysiology;joint decomposition;brain function;data-driven approaches;joint-ICA;multiple EEG electrodes;fMRI activation maps;ERP feature extraction;electroencephalography;functional magnetic resonance imaging;Electroencephalography;Electrodes;Integrated circuits;Visualization;Joints;Physiology;Data mining;Multimodal;EEG-fMRI;joint decomposition;jointICA},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569924695.pdf},
}

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