Minimum norm estimates based dipole source estimation

Konferenz: BIBE 2018 - International Conference on Biological Information and Biomedical Engineering
06.06.2018 - 08.06.2018 in Shanghai, China

Tagungsband: BIBE 2018

Seiten: 5Sprache: EnglischTyp: PDF

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Li, Ming-ai (Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China & Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, China)
Wang, Yi-fan; Sun, Yan-jun (Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China)

Motor Imagery EEG (MI-EEG) conceals a large amount of biological information in the sensorimotor cortex. Although sensor recognition mode benefits from the high time-frequency resolution of EEG, the decoding accuracy of complex MI-tasks is limited resulting from the finite scalp electrodes which contain partial available information of motor cortex in sensor domain. With the development of biomedical engineering, a spatial analysis technique of EEG Source Imaging (ESI) has generated to transform the EEG signals of low dimension into brain source space of high dimension. However, for the general inverse transformation of MI-EEG, the raw signals are previously decomposed to independent components, and the most relevant component to MI-tasks is selected to reconstruct the brain sources of active area. The dipole estimates generated through this process may not reflect all effective information of active motor cortex. Therefore we propose a novel Minimum Norm Estimates (MNE) based Dipole Source Estimation (DSE) integrated with overlapping averaging in the temporal domain in this paper. Based on a public dataset, abundant experiment results show that DSE can make full use of MI-EEG from entire time series and present more complete information of brain source with higher spatial resolution, which will contribute to the source recognition mode.