期刊
CURRENT OPINION IN BIOMEDICAL ENGINEERING
卷 18, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.cobme.2021.100277
关键词
Extent imaging; Electrophysiological source imaging; EEG; MEG; Sparsity; Bayesian framework
资金
- NIH [R01 NS096761, EB021027, MH114233, AT009263, EB029354]
The review discusses recent developments in signal processing and machine learning that have enabled noninvasive imaging of the extent, i.e. size, of underlying brain sources using scalp electromagnetic measurements.
Electrophysiological source imaging (ESI) has been successfully employed in many brain imaging applications during the last 20 years. ESI estimates of underlying brain networks provide millisecond resolution of dynamic brain processes; yet, it remains to be a challenge to further improve the spatial resolution of ESI modality, in particular on its capability of imaging the extent of underlying brain sources. In this review, we discuss the recent developments in signal processing and machine learning that have made it possible to image the extent, i.e. size, of underlying brain sources noninvasively, using scalp electromagnetic measurements from electroencephalogram (EEG) and magnetoencephalogram (MEG) recordings.
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