Dimensionality reduction of brain imaging data using graph signal processing

@article{Rui2016DimensionalityRO,
  title={Dimensionality reduction of brain imaging data using graph signal processing},
  author={Liu Rui and Hossein Nejati and Ngai-Man Cheung},
  journal={2016 IEEE International Conference on Image Processing (ICIP)},
  year={2016},
  pages={1329-1333}
}
Brain imaging data such as EEG or MEG is high-dimensional spatiotemporal measurements that commonly require dimensionality reduction before being used for further analysis or applications. This paper presents a new dimensionality reduction method based on the recent graph signal processing theory. Specifically, we focus on a task to classify the brain imaging signals recording the cortical activities in response to visual stimuli. We propose to use the resting-state measurements (i.e., before… CONTINUE READING

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