Functional magnetic resonance imaging (fMRI) "brain reading": detecting and classifying distributed patterns of fMRI activity in human visual cortex

By David D. Cox, Robert L. Savoy. NeuroImage, 2003

1,124 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/655dee46474502d4ee152c128241377164e91969

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In Cognitive Neuroscience

Citations in the corpus

Visualization of Nonlinear Classification Models in Neuroimaging - Signed Sensitivity Maps (2012, methodology): “Interest in applying multivariate analysis techniques to functional neuroimaging data is increasing, see e.g., (Lautrup et al., 1994; Mørch et al., 1997; Strother et al., 2002; Cox and Savoy, 2003; LaConte et al., 2005; O’Toole et al., 2007).”
Sparse estimation automatically selects voxels relevant for the decoding of fMRI activity patterns (2008, positive): “…of visual objects, which cannot be easily distinguished by the conventional functional mapping (see also Strother et al., 2002; Spiridon and Kanwisher, 2002; Cox and Savoy, 2003; Carlson et al., 2003; Mitchell et. al., 2004; Laconte et. al., 2005; O'Toole et al., 2005 for other examples).”
Bayesian mixed-effects inference on classification performance in hierarchical data sets (2012, methodology): “A third example is the ﬁeld of neuroimaging, where classiﬁers are used to relate subject-speciﬁc multivariate measures of brain activity to a particular cognitive or perceptual state (Cox and Savoy, 2003).”
Neuroimaging: Decoding mental states from brain activity in humans (2006, neutral): “However, this does not reveal whether such decoding of mental states, or 'brain reading”
Bayesian reconstruction of natural images from human brain activity (2009, positive): “This property of fMRI makes it an excellent tool for brain reading, in which the responses of multiple voxels are used to decode the stimulus that evoked them (Haxby et al., 2001; Carlson et al., 2002; Cox and Savoy, 2003; Haynes and Rees, 2005; Kamitani and Tong, 2005; Thirion et al., 2006; Kay et ”
Beyond mind-reading: multi-voxel pattern analysis of fMRI data. (2006, background): “Most MVPA studies have used linear classiﬁers, including correlation-based classiﬁers [16,17], neural networks without a hidden layer [29], linear discriminant analysis [19,22,24,28], linear support vector machines (SVMs) [20,23,26], and Gaussian Naive Bayes classiﬁers [26].”
PRoNTo: Pattern Recognition for Neuroimaging Toolbox (2013, methodology): “As examples, multivariate methods applied to fMRI have made it possible to decode the category of an object (Spiridon and Kanwisher 2002; Cox and Savoy 2003; Shinkareva et al. 2008) and orientation of a striped pattern (Haynes and Rees 2005; Kamitani and Tong 2005) visually presented to the…”
Visual image reconstruction from human brain activity using a combination of multiscale local image decoders. (2008, background): “…studies have shown that visual features, such as orientation and motion direction (Kamitani and Tong, 2005, 2006), and visual object categories (Cox and Savoy, 2003; Haxby et al., 2001) can be decoded from fMRI activity patterns by a statistical ‘‘de-coder,’’ which learns the mapping between a…”
Reading the mind's eye: decoding category information during mental imagery (2009, background): “In highertier areas, object-category information can be gleaned from groups of category-selective voxels as well as from more distributed representations (Carlson et al., 2003; Cox and Savoy, 2003; Haxby et al., 2001; Norman et al., 2006; Reddy and Kanwisher, 2007; Spiridon and Kanwisher, 2002).”
Classifying spatial patterns of brain activity with machine learning methods: Application to lie detection (2005, methodology): “, 1996), some of which, attempt to use these methods to classify complex activation patterns using machine learning methods (Cox and Savoy, 2003; LaConte et al., 2005).”
Predicting the orientation of invisible stimuli from activity in human primary visual cortex (2005, background): “Encouraged by recent reports that multivariate analyses can be used to successfully classify fMRI measurements of brain activity evoked by different object categorie”