Hierarchical latent dictionaries for models of brain activation

Conference Paper

In this work, we propose a hierarchical latent dictionary approach to estimate the timevarying mean and covariance of a process for which we have only limited noisy samples. We fully leverage the limited sample size and redundancy in sensor measurements by transferring knowledge through a hierarchy of lower dimensional latent processes. As a case study, we utilize Magnetoencephalography (MEG) recordings of brain activity to identify the word being viewed by a human subject. Specifically, we identify the word category for a single noisy MEG recording, when only given limited noisy samples on which to train.

Duke Authors

Cited Authors

  • Fyshe, A; Fox, E; Dunson, D; Mitchell, T

Published Date

  • January 1, 2012

Published In

Volume / Issue

  • 22 /

Start / End Page

  • 409 - 421

Electronic International Standard Serial Number (EISSN)

  • 1533-7928

International Standard Serial Number (ISSN)

  • 1532-4435

Citation Source

  • Scopus