Neuroimaging: Decoding mental states from brain activity in humans

By John-Dylan Haynes, Geraint E. Rees. Nature Reviews Neuroscience, 2006

1,738 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Social Psychology

View Paper: https://www.semanticscholar.org/paper/ee2d6e1cf14bc59b37ff8a85f926ecb55325fce8

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Citations in the corpus

PyMVPA: a Python Toolbox for Multivariate Pattern Analysis of fMRI Data (2009, methodology): “…- based analyses , harnessing machine - learning techniques based on statistical learning theory to extract information about the functional properties of the brain previously thought to be below the signal - to - noise ratio of fMRI data ( for reviews see Haynes and Rees , 2006 ; Norman et al .”
Encoding and decoding in fMRI (2010, methodology): “Finally, decoding models provide the foundation for potential brain-reading and neuroprosthetic technologies (deCharms, 2008; Haynes and Rees, 2006).”
Multivariate pattern analysis of fMRI: The early beginnings (2012, background): “…of review papers began to appear that presented the principles of MVPA in a clear and accessible manner, further demystifying the basis of this new analytic approach (Haynes and Rees, 2006; Mur et al., 2009; Norman et al., 2006; O'Toole et al., 2007; Pereira et al., 2009; Tong and Pratte, 2012).”
Imagine all the people: how the brain creates and uses personality models to predict behavior. (2014, methodology): “Moreover, to determine whether the brain represents unique personality profiles with different brain areas, we employed multivariate pattern analysis (MVPA) (Haynes and Rees 2006; Norman et al. 2006).”
Introduction to machine learning for brain imaging (2011, methodology): “…analysis tools that correlate single voxels with behavior to a full multivariate correlation analysis (e.g., Hansen et al., 1999; Haxby et al., 2001; Strother et al., 2002; Cox and Savoy, 2003; Kamitani and Tong, 2005; Haynes and Rees, 2006; Kriegeskorte et al., 2006; Mitchell et al., 2008).”
A Primer on Pattern-Based Approaches to fMRI: Principles, Pitfalls, and Perspectives. (2015, methodology): “Where appropriate, the reader is pointed to more in-depth reviews on specialized topics, such as cognitive neuroscience applications (Haynes and Rees, 2006; Norman et al., 2006; Tong and Pratte, 2012), neural coding (Haxby et al., 2014; Kriegeskorte, 2009; Kriegeskorte and Kievit, 2013; Naselaris…”
Machine learning for real-time single-trial EEG-analysis: from brain-computer interfacing to mental state monitoring. (2008, neutral): “…monitoring of generic brain states beyond voluntarily altered brain activity has in the past been under study e.g. for the detection of sleep stages, tiredness, arousal, for emotion monitoring and for cognitive workload analysis (Kohlmorgen et al., 2007; Haynes and Rees, 2006; Müller et al., 1995).”
A Toolbox for Representational Similarity Analysis (2014, background): “decoding analyses have helped reveal what information is present for linear readout in each region [2–11].”
Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation. (2017, background): “Although the distinction between higher-order and lowerorder functions is not clear cut, by convention BMIs are called cognitive if they work in the domains of cognitive states (348, 886), executive functions (349), decision making (21, 335, 343), memory (62, 63), attention (35, 417), and language (”
Decoding brain states from fMRI connectivity graphs (2011, methodology): “More recently, approaches derived from supervised machine learning – commonly termed “ brain decoding ” in the ﬁ eld of neuroimaging – have shown that it is possible to exploit more subtle relationships in voxels' intensity patterns (Haxby et al., 2001; Haynes and Rees, 2006; Norman et al., 2006).”
Sparse estimation automatically selects voxels relevant for the decoding of fMRI activity patterns (2008, neutral): “For comprehensive reviews, see Haynes and Rees (2006) and Norman et al. (2006).”
New Scanner Data for Brand Marketers: How Neuroscience Can Help Better Understand Differences in Brand Preferences (2012, background): “…snack foods can be modulated by contextual cues (Hare, Malmaud, & Rangel, 2011), improving the ability to identify individuals with weaker preferences (i.e., those more likely to susceptible to contextual changes in a decision environment) would be of tremendous value to marketing researchers.”
Representational Similarity Analysis – Connecting the Branches of Systems Neuroscience (2008, neutral): “…2006; Polyn et al., 2005; Serences and Boynton, 2007; Spiridon and Kanwisher, 2002; Strother et al., 2002; Williams et al., 2007; for reviews see Haynes and Rees, 2006; Kriegeskorte and Bandettini, 2007; Norman et al., 2006) and also in monkey electrophysiology (Hung et al., 2005; Tsao et al.,…”