Filters








1,207 Hits in 5.9 sec

Generalized group sparse classifiers with application in fMRI brain decoding

Bernard Ng, Rafeef Abugharbieh
2011 CVPR 2011  
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

Mohammad Hadi Kefayati, Hamid Sheikhzadeh, Hamid R. Rabiee, Ali Soltani-Farani
2013 2013 International Workshop on Pattern Recognition in Neuroimaging  
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]

Bernard Ng, Arash Vahdat, Ghassan Hamarneh, Rafeef Abugharbieh
2010 Lecture Notes in Computer Science  
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

Bernard Ng, Rafeef Abugharbieh
2011 2011 International Workshop on Pattern Recognition in NeuroImaging  
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

Jing Zhang, Chuncheng Zhang, Li Yao, Xiaojie Zhao, Zhiying Long
2018 Computational Intelligence and Neuroscience  
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

Daniel Callan, Lloyd Mills, Connie Nott, Robert England, Shaun England, Wang Zhan
2014 PLoS ONE  
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]

Hongming Li, Yong Fan
2018 Lecture Notes in Computer Science  
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]

Bernard Ng, Rafeef Abugharbieh
2011 Lecture Notes in Computer Science  
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

Bing Du, Xiaomu Cheng, Yiping Duan, Huansheng Ning
2022 Brain Sciences  
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

Philip A. Kragel, R. McKell Carter, Scott A. Huettel
2012 Frontiers in Neuroscience  
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

Dongha Lee, Sungjae Yun, Changwon Jang, Hae-Jeong Park, Suliann Ben Hamed
2017 PLoS ONE  
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

Zhi Yang, Fang Fang, Xuchu Weng
2012 Neuroscience Bulletin  
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

Bryan R. Conroy, Jennifer M. Walz, Paul Sajda, Xia Wu
2013 PLoS ONE  
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

Natsue Yoshimura, Atsushi Nishimoto, Abdelkader Nasreddine Belkacem, Duk Shin, Hiroyuki Kambara, Takashi Hanakawa, Yasuharu Koike
2016 Frontiers in Neuroscience  
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

Alejandra Mejia Tobar, Rikiya Hyoudou, Kahori Kita, Tatsuhiro Nakamura, Hiroyuki Kambara, Yousuke Ogata, Takashi Hanakawa, Yasuharu Koike, Natsue Yoshimura
2018 Frontiers in Neuroscience  
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
« Previous Showing results 1 — 15 out of 1,207 results