Comput Math Methods Med - Principal feature analysis: a multivariate feature selection method for fMRI data.

Tópicos

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Resumo

Brain decoding with functional magnetic resonance imaging (fMRI) requires analysis of complex, multivariate data. Multivoxel pattern analysis (MVPA) has been widely used in recent years. MVPA treats the activation of multiple voxels from fMRI data as a pattern and decodes brain states using pattern classification methods. Feature selection is a critical procedure of MVPA because it decides which features will be included in the classification analysis of fMRI data, thereby improving the performance of the classifier. Features can be selected by limiting the analysis to specific anatomical regions or by computing univariate (voxel-wise) or multivariate statistics. However, these methods either discard some informative features or select features with redundant information. This paper introduces the principal feature analysis as a novel multivariate feature selection method for fMRI data processing. This multivariate approach aims to remove features with redundant information, thereby selecting fewer features, while retaining the most information.

Resumo Limpo

brain decod function magnet reson imag fmri requir analysi complex multivari data multivoxel pattern analysi mvpa wide use recent year mvpa treat activ multipl voxel fmri data pattern decod brain state use pattern classif method featur select critic procedur mvpa decid featur will includ classif analysi fmri data therebi improv perform classifi featur can select limit analysi specif anatom region comput univari voxelwis multivari statist howev method either discard inform featur select featur redund inform paper introduc princip featur analysi novel multivari featur select method fmri data process multivari approach aim remov featur redund inform therebi select fewer featur retain inform

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