A sparse dictionary learning framework to discover discriminative source activations in EEG brain mapping

Feng Liu, Shouyi Wang, Jay Rosenberger, Jianzhong Su, Hanli Liu

Research output: Contribution to conferencePaperpeer-review

13 Scopus citations

Abstract

Electroencephalography (EEG) source analysis is one of the most important noninvasive human brain imaging tools that provides millisecond temporal accuracy. However, discovering essential activated brain sources associated with different brain status is still a challenging problem. In this study, we propose for the first time that the ill-posed EEG inverse problem can be formulated and solved as a sparse over-complete dictionary learning problem. In particular, a novel supervised sparse dictionary learning framework was developed for EEG source reconstruction. A revised version of discriminative K-SVD (DK-SVD) algorithm is exploited to solve the formulated supervised dictionary learning problem. As the proposed learning framework incorporated the EEG label information of different brain status, it is capable of learning a sparse representation that reveal the most discriminative brain activity sources among different brain states. Compared to the state-of-the-art EEG source analysis methods, proposed sparse dictionary learning framework achieved significant superior performance in both computing speed and accuracy for the challenging EEG source reconstruction problem through extensive numerical experiments. More importantly, the experimental results also validated that the proposed sparse learning framework is effective to discover the discriminative task-related brain activation sources, which shows the potential to advance the high resolution EEG source analysis for real-time non-invasive brain imaging research.

Original languageEnglish
Pages1431-1437
Number of pages7
StatePublished - 2017
Event31st AAAI Conference on Artificial Intelligence, AAAI 2017 - San Francisco, United States
Duration: 4 Feb 201710 Feb 2017

Conference

Conference31st AAAI Conference on Artificial Intelligence, AAAI 2017
Country/TerritoryUnited States
CitySan Francisco
Period4/02/1710/02/17

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