@InProceedings{7362709,
  author = {B. Belaoucha and J. Lina and M. Clerc and T. Papadopoulo},
  booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},
  title = {MEM-diffusion MRI framework to solve MEEG inverse problem},
  year = {2015},
  pages = {1875-1879},
  abstract = {In this paper, we present a framework to fuse information coming from diffusion magnetic resonance imaging (dMRI) with Magnetoencephalography (MEG)/Electroencephalography (EEG) measurements to reconstruct the activation on the cortical surface. The MEG/EEG inverse-problem is solved by the Maximum Entropy on the Mean (MEM) principle and by assuming that the sources inside each cortical region follow Normal distribution. These regions are obtained using dMRI and assumed to be functionally independent. The source reconstruction framework presented in this work is tested using synthetic and real data. The activated regions for the real data is consistent with the literature about the face recognition and processing network.},
  keywords = {biodiffusion;biomedical MRI;electroencephalography;image reconstruction;magnetoencephalography;medical image processing;MEM-dMRI framework;diffusion MRI;magnetic resonance imaging;MEEG measurements;magnetoencephalography;electroencephalography;cortical surface;Maximum Entropy on the Mean;MEM principle;source reconstruction framework;face recognition;processing network;Electroencephalography;Entropy;Magnetic resonance imaging;Face recognition;Time measurement;Europe;Signal processing;MEG;EEG;dMRI;source reconstruction;parcellation;MEM},
  doi = {10.1109/EUSIPCO.2015.7362709},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570103657.pdf},
}

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