Dynamic network modeling and dimensionality reduction for human ECoG activity.

Journal: Journal of neural engineering
Published Date: Aug 14, 2019

Abstract

OBJECTIVE: Developing dynamic network models for multisite electrocorticogram (ECoG) activity can help study neural representations and design neurotechnologies in humans given the clinical promise of ECoG. However, dynamic network models have so far largely focused on spike recordings rather than ECoG. A dynamic network model for ECoG recordings, which constitute a network, should describe their temporal dynamics while also achieving dimensionality reduction given the inherent spatial and temporal correlations.

Authors

  • Yuxiao Yang
    Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America.
  • Omid G Sani
  • Edward F Chang
    Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA. Electronic address: edward.chang@ucsf.edu.
  • Maryam M Shanechi

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