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Evaluating Graph Signal Processing for Neuroimaging Through Classification and Dimensionality Reduction

M. Ménoret, N. Farrugia, B. Pasdeloup and V. Gripon, "Evaluating Graph Signal Processing for Neuroimaging Through Classification and Dimensionality Reduction," in Proceedings of GlobalSip, 2017. To appear.

Graph Signal Processing (GSP) is a promising framework to analyze multi-dimensional neuroimaging datasets, while taking into account both the spatial and functional dependencies between brain signals. In the present work, we apply dimensionality reduction techniques based on graph representations of the brain to decode brain activity from real and simulated fMRI datasets. We introduce seven graphs obtained from a) geometric structure and/or b) functional connectivity between brain areas at rest, and compare them when performing dimension reduction for classification. We show that mixed graphs using both a) and b) offer the best performance. We also show that graph sampling methods perform better than classical dimension reduction including Principal Component Analysis (PCA) and Independent Component Analysis (ICA).

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Bibtex
@inproceedings{MéFarPasGri2017,
  author = {Mathilde Ménoret and Nicolas Farrugia and
Bastien Pasdeloup and Vincent Gripon},
  title = {Evaluating Graph Signal Processing for
Neuroimaging Through Classification and Dimensionality
Reduction},
  booktitle = {Proceedings of GlobalSip},
  year = {2017},
  note = {To appear},
}




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