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• W4288391583 abstract "Considering that functional magnetic resonance imaging (fMRI) signals from multiple subjects (MS) can be represented together as a sum of common and a sum of distinct rank-1 matrices, a new MS dictionary learning (DL) algorithm named sparse group (common + distinct) bases (sgBACES) is proposed. Unlike existing MS-DL algorithms that ignore fMRI data&#x2019;s prior information, it is formulated as a penalized plus constrained rank-1 matrix approximation, where <i>l</i>₁ norm-based adaptive sparse penalty, <i>l</i>₀ norm-based dictionary regularization, and lag-1 based autocorrelation maximization have been introduced in the minimization problem. Besides, spatial dependence among voxels has been exploited for fine-tuning the sparsity parameters. To my best knowledge, the sgBACES algorithm is the first to effectively take both temporal and spatial prior information into account for an MS-fMRI-DL framework. It also has the advantage of not requiring a separate sparse coding stage. Studies based on synthetic and experimental fMRI datasets are used to compare the performance of sgBACES with the state-of-the-art algorithms in terms of correlation strength and computation time. It emerged that the proposed sgBACES algorithm enhanced the signal-to-noise ratio (SNR) of the recovered time courses (TCs) and the precision of the recovered spatial maps (SMs). A 9.2% increase in correlation value over the ShSSDL algorithm is observed for motor-task based fMRI data." @default.
• W4288391583 created "2022-07-29" @default.
• W4288391583 creator A5011000454 @default.
• W4288391583 date "2022-01-01" @default.
• W4288391583 modified "2023-09-23" @default.
• W4288391583 title "Sparse Group Bases for Multisubject fMRI Data" @default.
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• W4288391583 doi "https://doi.org/10.1109/access.2022.3194651" @default.
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