177 related articles for article (PubMed ID: 30970029)
1. Two-step paretial least square regression classifiers in brain-state decoding using functional magnetic resonance imaging.
Long Z; Wang Y; Liu X; Yao L
PLoS One; 2019; 14(4):e0214937. PubMed ID: 30970029
[TBL] [Abstract][Full Text] [Related]
2. Improved Application of Sparse Representation Classifier in fMRI-based Brain State Decoding.
Guo Z; Long Z; Zhang J; Xia M; Yao L
Annu Int Conf IEEE Eng Med Biol Soc; 2018 Jul; 2018():5523-5526. PubMed ID: 30441588
[TBL] [Abstract][Full Text] [Related]
3. Quantitative modeling of the neural representation of objects: how semantic feature norms can account for fMRI activation.
Chang KM; Mitchell T; Just MA
Neuroimage; 2011 May; 56(2):716-27. PubMed ID: 20451625
[TBL] [Abstract][Full Text] [Related]
4. The effect of spatial smoothing on fMRI decoding of columnar-level organization with linear support vector machine.
Misaki M; Luh WM; Bandettini PA
J Neurosci Methods; 2013 Jan; 212(2):355-61. PubMed ID: 23174092
[TBL] [Abstract][Full Text] [Related]
5. Principal feature analysis: a multivariate feature selection method for fMRI data.
Wang L; Lei Y; Zeng Y; Tong L; Yan B
Comput Math Methods Med; 2013; 2013():645921. PubMed ID: 24171045
[TBL] [Abstract][Full Text] [Related]
6. Multiclass fMRI data decoding and visualization using supervised self-organizing maps.
Hausfeld L; Valente G; Formisano E
Neuroimage; 2014 Aug; 96():54-66. PubMed ID: 24531045
[TBL] [Abstract][Full Text] [Related]
7. Improved sparse decomposition based on a smoothed L0 norm using a Laplacian kernel to select features from fMRI data.
Zhang C; Song S; Wen X; Yao L; Long Z
J Neurosci Methods; 2015 Apr; 245():15-24. PubMed ID: 25681758
[TBL] [Abstract][Full Text] [Related]
8. Comparison of multivariate classifiers and response normalizations for pattern-information fMRI.
Misaki M; Kim Y; Bandettini PA; Kriegeskorte N
Neuroimage; 2010 Oct; 53(1):103-18. PubMed ID: 20580933
[TBL] [Abstract][Full Text] [Related]
9. Decoding of visual activity patterns from fMRI responses using multivariate pattern analyses and convolutional neural network.
Zafar R; Kamel N; Naufal M; Malik AS; Dass SC; Ahmad RF; Abdullah JM; Reza F
J Integr Neurosci; 2017; 16(3):275-289. PubMed ID: 28891512
[TBL] [Abstract][Full Text] [Related]
10. Decoding and mapping task states of the human brain via deep learning.
Wang X; Liang X; Jiang Z; Nguchu BA; Zhou Y; Wang Y; Wang H; Li Y; Zhu Y; Wu F; Gao JH; Qiu B
Hum Brain Mapp; 2020 Apr; 41(6):1505-1519. PubMed ID: 31816152
[TBL] [Abstract][Full Text] [Related]
11. Pairwise Classifier Ensemble with Adaptive Sub-Classifiers for fMRI Pattern Analysis.
Kim E; Park H
Neurosci Bull; 2017 Feb; 33(1):41-52. PubMed ID: 27838826
[TBL] [Abstract][Full Text] [Related]
12. Brain State Decoding Based on fMRI Using Semisupervised Sparse Representation Classifications.
Zhang J; Zhang C; Yao L; Zhao X; Long Z
Comput Intell Neurosci; 2018; 2018():3956536. PubMed ID: 29849545
[TBL] [Abstract][Full Text] [Related]
13. Euler Elastica Regularized Logistic Regression for Whole-Brain Decoding of fMRI Data.
Zhang C; Yao L; Song S; Wen X; Zhao X; Long Z
IEEE Trans Biomed Eng; 2018 Jul; 65(7):1639-1653. PubMed ID: 28952931
[TBL] [Abstract][Full Text] [Related]
14. Pattern classification of fMRI data: applications for analysis of spatially distributed cortical networks.
Yourganov G; Schmah T; Churchill NW; Berman MG; Grady CL; Strother SC
Neuroimage; 2014 Aug; 96():117-32. PubMed ID: 24705202
[TBL] [Abstract][Full Text] [Related]
15. Decoding the individual finger movements from single-trial functional magnetic resonance imaging recordings of human brain activity.
Shen G; Zhang J; Wang M; Lei D; Yang G; Zhang S; Du X
Eur J Neurosci; 2014 Jun; 39(12):2071-82. PubMed ID: 24661456
[TBL] [Abstract][Full Text] [Related]
16. An empirical comparison of different LDA methods in fMRI-based brain states decoding.
Xia M; Song S; Yao L; Long Z
Biomed Mater Eng; 2015; 26 Suppl 1():S1185-92. PubMed ID: 26405876
[TBL] [Abstract][Full Text] [Related]
17. Multivariate detrending of fMRI signal drifts for real-time multiclass pattern classification.
Lee D; Jang C; Park HJ
Neuroimage; 2015 Mar; 108():203-13. PubMed ID: 25573669
[TBL] [Abstract][Full Text] [Related]
18. Comparing within-subject classification and regularization methods in fMRI for large and small sample sizes.
Churchill NW; Yourganov G; Strother SC
Hum Brain Mapp; 2014 Sep; 35(9):4499-517. PubMed ID: 24639383
[TBL] [Abstract][Full Text] [Related]
19. Spatially regularized machine learning for task and resting-state fMRI.
Song X; Panych LP; Chen NK
J Neurosci Methods; 2016 Jan; 257():214-28. PubMed ID: 26470627
[TBL] [Abstract][Full Text] [Related]
20. Accuracy of automated classification of major depressive disorder as a function of symptom severity.
Ramasubbu R; Brown MR; Cortese F; Gaxiola I; Goodyear B; Greenshaw AJ; Dursun SM; Greiner R
Neuroimage Clin; 2016; 12():320-31. PubMed ID: 27551669
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
