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Detecting cortical activities from fMRI time‐course data using the music algorithm with forward and backward covariance averaging
Author(s) -
Sekihara Kensuke,
Koizumi Hideaki
Publication year - 1996
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.1910350604
Subject(s) - covariance , covariance matrix , algorithm , computer science , artificial intelligence , signal (programming language) , signal processing , pattern recognition (psychology) , field (mathematics) , covariance function , resolution (logic) , mathematics , digital signal processing , statistics , computer hardware , programming language , pure mathematics
A method is proposed for processing time‐course fMRI data taken wKh successlve single‐shot echo‐planar imaging. The proposed method uses a two‐dimensional version of the multiple signal classification (MUSIC) algorithm and the technique called covariance averaging, both of which were developed in the field of sensor‐array processing. The proposed method consists of four steps: calculate the averaged data covariance matrix, determine the number of activities using this covariance matrix, estimate the locations of the activities, and estimate their time evolution curves. Computer simulation resutts showed that a nearly fourfold improvement in the spatial resolution can be attained due to the method's super‐resolution capability.