Supervised discriminative EEG brain source imaging with graph regularization

Feng Liu, Rahilsadat Hosseini, Jay Rosenberger, Shouyi Wang, Jianzhong Su

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

4 Scopus citations

Abstract

As Electroencephalography (EEG) is a non-invasive brain imaging technique that records the electric field on the scalp instead of direct measuring activities of brain voxels on the cortex, many approaches were proposed to estimate the activated sources due to its significance in neuroscience research and clinical applications. However, since most part of the brain activity is composed of the spontaneous neural activities or non-task related activations, true task relevant activation sources can be very challenging to be discovered given strong background signals. For decades, the EEG source imaging problem was solved in an unsupervised way without taking into consideration the label information that representing different brain states (e.g. happiness, sadness, and surprise). A novel model for solving EEG inverse problem called Graph Regularized Discriminative Source Imaging (GRDSI) was proposed, which aims to explicitly extract the discriminative sources by implicitly coding the label information into the graph regularization term. The proposed model is capable of estimating the discriminative brain sources under different brain states and encouraging intra-class consistency. Simulation results show the effectiveness of our proposed framework in retrieving the discriminative sources.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings
EditorsMaxime Descoteaux, Simon Duchesne, Alfred Franz, Pierre Jannin, D. Louis Collins, Lena Maier-Hein
Pages495-504
Number of pages10
DOIs
StatePublished - 2017
Event20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017 - Quebec City, Canada
Duration: 11 Sep 201713 Sep 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10433 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017
Country/TerritoryCanada
CityQuebec City
Period11/09/1713/09/17

Keywords

  • EEG source imaging
  • Graph regularization
  • Inverse problem
  • Sparse representation

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