Supervised EEG Source Imaging with Graph Regularization in Transformed Domain

Feng Liu, Jing Qin, Shouyi Wang, Jay Rosenberger, Jianzhong Su

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations


It is of great significance to infer activation extents under different cognitive tasks in neuroscience research as well as clinical applications. However, the EEG electrodes measure electrical potentials on the scalp instead of directly measuring activities of brain sources. To infer the activated cortex sources given the EEG data, many approaches were proposed with different neurophysiological assumptions. Traditionally, the EEG inverse problem was solved in an unsupervised way without any utilization of the brain status label information. We propose that by leveraging label information, the task related discriminative extended source patches can be much better retrieved from strong spontaneous background signals. In particular, to find task related source extents, a novel supervised EEG source imaging model called Graph regularized Variation-Based Sparse Cortical Current Density (GVB-SCCD) was proposed to explicitly extract the discriminative source extents by embedding the label information into the graph regularization term. The graph regularization was derived from the constraint that requires consistency for all the solutions on different time points within the same class. An optimization algorithm based on the alternating direction method of multipliers (ADMM) is derived to solve the GVB-SCCD model. Numerical results show the effectiveness of our proposed framework.

Original languageEnglish
Title of host publicationBrain Informatics - International Conference, BI 2017, Proceedings
EditorsYi Zeng, Bo Xu, Maryann Martone, Yong He, Hanchuan Peng, Qingming Luo, Jeanette Hellgren Kotaleski
Number of pages13
StatePublished - 2017
EventInternational Conference on Brain Informatics, BI 2017 - Beijing, China
Duration: Nov 16 2017Nov 18 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10654 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Conference on Brain Informatics, BI 2017

Bibliographical note

Publisher Copyright:
© 2017, Springer International Publishing AG.


  • Alternating direction method of multiplier (ADMM)
  • Discriminative source
  • EEG source imaging
  • Graph regularization
  • Total variation (TV)

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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