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Generalized group sparse classifiers with application in fMRI brain decoding
2011
CVPR 2011
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In this paper, we present a novel formulation for constructing "Generalized Group Sparse Classifiers" (GSSC) to alleviate these problems. ...
In the context of fMRI, GGSC provides a flexible means for modeling how the brain is functionally organized into specialized modules (i.e. groups of voxels) with spatially proximal voxels often displaying ...
We refer to classifiers built from our formulation as "Generalized Group Sparse Classifiers" (GGSC), which can be viewed as a group level extension of our recently proposed "Generalized Sparse Classifiers ...
doi:10.1109/cvpr.2011.5995651
dblp:conf/cvpr/NgA11
fatcat:ng2un33wmvckjeldnugjibicyu
Semi-spatiotemporal fMRI Brain Decoding
2013
2013 International Workshop on Pattern Recognition in Neuroimaging
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Degradation in brain decoding performance due to lack of temporal information in the model, 2. Inability to provide temporal interpretability. ...
Even though fMRI signals have temporal and spatial structures, most studies have neglected the temporal structure when inferring mental states (brain decoding). This has two main side effects: 1. ...
Bernard Ng for helpful discussions on the spatiotemporal generalized sparse classifiers. This work was supported in part by the AICT Research Center. ...
doi:10.1109/prni.2013.54
dblp:conf/prni/KefayatiSRS13
fatcat:pcgyhxe7gnbvllkglyto6muyxm
Generalized Sparse Classifiers for Decoding Cognitive States in fMRI
[chapter]
2010
Lecture Notes in Computer Science
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In this paper, we propose a new group of classifiers, "Generalized Sparse Classifiers" (GSC), to alleviate this overfitting problem. ...
We validate on real fMRI data and demonstrate how explicitly modeling spatial correlations inherent in brain activity using GSC can provide superior predictive performance and interpretability over standard ...
Conclusion In this paper, we proposed a new group of classifiers, "Generalized Sparse Classifiers," for performing large-scale classification problems such as those seen in fMRI studies. ...
doi:10.1007/978-3-642-15948-0_14
fatcat:2xgs6wxqorblvggqztehtwhvnm
Modeling Spatiotemporal Structure in fMRI Brain Decoding Using Generalized Sparse Classifiers
2011
2011 International Workshop on Pattern Recognition in NeuroImaging
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Coupled with the typically strong noise in fMRI data, prediction accuracy is often limited. ...
To impose a spatiotemporal prior, we employ a recent classifier learning formulation for building Generalized Sparse Classifiers (GSC). ...
Generalized Sparse Classifier To integrate application-specific properties beyond mere sparsity into classifier learning, we [12] proposed combining a generalized ridge term with the least absolute shrinkage ...
doi:10.1109/prni.2011.10
dblp:conf/prni/NgA11
fatcat:svkepjautfapnosaocbtzbzwyi
Brain State Decoding Based on fMRI Using Semisupervised Sparse Representation Classifications
2018
Computational Intelligence and Neuroscience
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Among various classification techniques, sparse representation classifier (SRC) exhibits a state-of-the-art classification performance in image classification. ...
Multivariate classification techniques have been widely applied to decode brain states using functional magnetic resonance imaging (fMRI). ...
Litao Zhu for technical assistance with real fMRI data acquisition. ...
doi:10.1155/2018/3956536
pmid:29849545
pmcid:PMC5933074
fatcat:pmckc5gbmfcutlhbkk4ijxthj4
A Tool for Classifying Individuals with Chronic Back Pain: Using Multivariate Pattern Analysis with Functional Magnetic Resonance Imaging Data
2014
PLoS ONE
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We analyzed contrast fMRI images in stimulation versus rest in pain-related brain regions to distinguish between the groups of participants: 1) chronic pain and 2) normal controls. ...
This method of data analysis was very successful in correctly classifying each of the two groups. ...
The general applicability of the classifier would be greatly enhanced if it were found that different types of stimulation could be used for testing even though the classifier is only trained on a specific ...
doi:10.1371/journal.pone.0098007
pmid:24905072
pmcid:PMC4048172
fatcat:fwovjbu6efap7fmxoghqz4pvhu
Brain Decoding from Functional MRI Using Long Short-Term Memory Recurrent Neural Networks
[chapter]
2018
Lecture Notes in Computer Science
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Decoding brain functional states underlying different cognitive processes using multivariate pattern recognition techniques has attracted increasing interests in brain imaging studies. ...
In this study, we develop a deep learning based framework for brain decoding by leveraging recent advances in sequence modeling using long short-term memory (LSTM) recurrent neural networks (RNNs). ...
Acknowledgements This work was supported in part by National Institutes of Health grants [CA223358, EB022573, DK114786, DA039215, and DA039002] and a NVIDIA Academic GPU grant. ...
doi:10.1007/978-3-030-00931-1_37
pmid:30320311
pmcid:PMC6180332
fatcat:w2f675q4jbd5tjwfxak2kihv3q
Generalized Sparse Regularization with Application to fMRI Brain Decoding
[chapter]
2011
Lecture Notes in Computer Science
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In this paper, we propose a simple approach, generalized sparse regularization (GSR), for incorporating domain-specific knowledge into a wide range of sparse linear models, such as the LASSO and group ...
We demonstrate the power of GSR by building anatomically-informed sparse classifiers that additionally model the intrinsic spatiotemporal characteristics of brain activity for fMRI classification. ...
To build a sparse LDA classifier that is only anatomically-informed (ASLDA), we set λ in (11) to 0 with J(a) being the group LASSO penalty (6) . ...
doi:10.1007/978-3-642-22092-0_50
fatcat:v62akdnolvdjlh5pu5v36ug7ny
fMRI Brain Decoding and Its Applications in Brain–Computer Interface: A Survey
2022
Brain Sciences
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Furthermore, brain neural-activity-decoding-enabled fMRI-based BCI applications in mental and psychological disease treatment are presented to illustrate the positive correlation between brain decoding ...
With the great success of deep learning on image recognition and generation, deep neural networks (DNN) have been engaged in reconstructing visual stimuli from human brain activity via functional magnetic ...
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. ...
doi:10.3390/brainsci12020228
pmid:35203991
pmcid:PMC8869956
fatcat:t664eccq6nh5plnvhac2r2gcpa
What Makes a Pattern? Matching Decoding Methods to Data in Multivariate Pattern Analysis
2012
Frontiers in Neuroscience
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Research in neuroscience faces the challenge of integrating information across different spatial scales of brain function. ...
While the prevalence of MVPA has increased dramatically in recent years, its typical implementations for classification of mental states utilize only a subset of the information encoded in local fMRI signals ...
Huettel was supported by an Incubator Award from the Duke Institute for Brain Sciences. ...
doi:10.3389/fnins.2012.00162
pmid:23189035
pmcid:PMC3505006
fatcat:nfv6ozoz4zeqzdyyacqdzwmqme
Multivariate Bayesian decoding of single-trial event-related fMRI responses for memory retrieval of voluntary actions
2017
PLoS ONE
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The proposed method for decoding subject-specific memory retrieval of voluntary behavior using fMRI would be useful in forensic applications in a natural environment, where many known trials can be extracted ...
To overcome such problems, we proposed a single trial-based classification method of rapid event-related fMRI signals utilizing sparse multivariate Bayesian decoding of spatio-temporal fMRI responses. ...
Introduction Decoding brain states using functional magnetic resonance imaging (fMRI) has long been applied in various research areas; for example, fMRI is used to identify explicit responses in vision ...
doi:10.1371/journal.pone.0182657
pmid:28777830
pmcid:PMC5544208
fatcat:bdsvbkrthnhcfa2oztgkrkomca
Recent developments in multivariate pattern analysis for functional MRI
2012
Neuroscience Bulletin
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Compared with the traditional univariate methods, MVPA is more sensitive to subtle changes in multivariate patterns in fMRI data. ...
In this review, we introduce several significant advances in MVPA applications and summarize various combinations of algorithms and parameters in different problem settings. ...
Acknowledgements: We thank Professor Georg Northoff for helpful comments in the preparation of the manuscript. ...
doi:10.1007/s12264-012-1253-3
pmid:22833038
pmcid:PMC5561894
fatcat:b7w5olxdbbdyhap26whh6lj6p4
Fast Bootstrapping and Permutation Testing for Assessing Reproducibility and Interpretability of Multivariate fMRI Decoding Models
2013
PLoS ONE
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Multivariate decoding models are increasingly being applied to functional magnetic imaging (fMRI) data to interpret the distributed neural activity in the human brain. ...
We demonstrate our approach using full-brain analysis of elastic-net classifiers trained to discriminate stimulus type in an auditory and visual oddball event-related fMRI design. ...
Alternatively, a number of groups have applied sparse regression models to full-brain fMRI analysis [13, 14] , which allows for feature selection and classification to be performed simultaneously. ...
doi:10.1371/journal.pone.0079271
pmid:24244465
pmcid:PMC3828388
fatcat:kjoaz2txnncevk4lsbqyv5ptia
Decoding of Covert Vowel Articulation Using Electroencephalography Cortical Currents
2016
Frontiers in Neuroscience
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Applying a sparse logistic regression (SLR) method to classify the three tasks, mean classification accuracy using EEG cortical currents was significantly higher than that using EEG sensor signals and ...
EEG cortical currents were estimated with a variational Bayesian method that uses functional magnetic resonance imaging (fMRI) data as a hierarchical prior. ...
Koji Jimura of Keio University for his assistance with the fMRI experiments. ...
doi:10.3389/fnins.2016.00175
pmid:27199638
pmcid:PMC4853397
fatcat:qnlfneirojborefvtrty5pfkri
Decoding of Ankle Flexion and Extension from Cortical Current Sources Estimated from Non-invasive Brain Activity Recording Methods
2018
Frontiers in Neuroscience
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limb in patients with stroke, or BCIs from motor imagery in patients with spinal cord injury. ...
The hierarchical prior for the current source estimation from EEG was obtained from activated brain areas and their intensities from an fMRI group (second-level) analysis. ...
representation of the true current sources (group of neurons in each voxel) generated in the brain cortex. ...
doi:10.3389/fnins.2017.00733
pmid:29358903
pmcid:PMC5766671
fatcat:yxtismilgndwppsmhpox7gq5sy
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