Inter‐subject alignment of MEG datasets in a common representational space

Pooling neural imaging data across subjects requires aligning recordings from different subjects. In magnetoencephalography (MEG) recordings, sensors across subjects are poorly correlated both because of differences in the exact location of the sensors, and structural and functional differences in t...

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Veröffentlicht in:	Human brain mapping Jg. 38; H. 9; S. 4287 - 4301
Hauptverfasser:	Zhang, Qiong, Borst, Jelmer P., Kass, Robert E., Anderson, John R.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States John Wiley & Sons, Inc 01.09.2017 John Wiley and Sons Inc
Schlagworte:	Algorithms Alignment Anatomy Brain Brain - physiology Brain Mapping - methods Bypasses canonical correlation analysis common representational space Computer Simulation Correlation analysis Humans Inverse problems Localization Magnetoencephalography Magnetoencephalography - methods Multivariate Analysis Neuroimaging Position (location) Principal Component Analysis Regularization Sensors Structure-function relationships subject alignment common representational space canonical correlation analysis magnetoencephalography subject alignment
ISSN:	1065-9471, 1097-0193, 1097-0193
Online-Zugang:	Volltext
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