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Mixed-Norm Regularization for Brain Decoding
2014
Computational and Mathematical Methods in Medicine
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This work investigates the use of mixed-norm regularization for sensor selection in event-related potential (ERP) based brain-computer interfaces (BCI). ...
The different regularization approaches are compared on three ERP datasets showing the interest of mixed-norm regularization in terms of sensor selection. ...
[34] by studying general mixed-norms and proposed the use of the adaptive mixed-norms as sparsity-inducing regularizers. ...
doi:10.1155/2014/317056
pmid:24860614
pmcid:PMC4016929
fatcat:ioaym4qnnze5bl6g7wak7777he
Mixed-norm Regularization for Brain Decoding
[article]
2014
arXiv
pre-print
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This work investigates the use of mixed-norm regularization for sensor selection in Event-Related Potential (ERP) based Brain-Computer Interfaces (BCI). ...
The different regularization approaches are compared on three ERP datasets showing the interest of mixed-norm regularization in terms of sensor selection. ...
[34] by studying general mixed-norms and proposed the use of the adaptive mixed-norms as sparsity-inducing regularizers. ...
arXiv:1403.3628v1
fatcat:lt2hfhnugffzbobfb3i5dzp2ai
A Mixed L2 Norm Regularized HRF Estimation Method for Rapid Event-Related fMRI Experiments
2013
Computational and Mathematical Methods in Medicine
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Regularization methods have been widely used to increase the efficiency of HRF estimates. In this study, we propose a regularization framework called mixed L2 norm regularization. ...
This framework involves Tikhonov regularization and an additional L2 norm regularization term to calculate reliable HRF estimates. ...
Hence, we add an additional L2 regularization component into the estimation model with Tikhonov regularization, called mixed L2 norm (MN) regularization. ...
doi:10.1155/2013/643129
pmid:23762193
pmcid:PMC3665251
fatcat:je7xo573pnfazmszuvvukgkmpm
Sturm: Sparse Tubal-Regularized Multilinear Regression for fMRI
[article]
2018
arXiv
pre-print
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Specifically, the Sturm model performs multilinear regression with two regularization terms: a tubal tensor nuclear norm based on t-SVD and a standard L1 norm. ...
We perform experiments on four classification problems, including both resting-state fMRI for disease diagnosis and task-based fMRI for neural decoding. ...
It is commonly used for studies decoding brain cognitive states, or neural decoding. The objective is to classify (decode) the tasks performed by subjects using the fMRI information. ...
arXiv:1812.01496v1
fatcat:llyqce27jrdgba3j7dkk6nofgm
Finding distributed needles in neural haystacks
2020
Journal of Neuroscience
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and whole-brain decoding with L1 or L2 regularization-each have critical and complementary blind spots under these conditions. ...
For faces and possibly other domains, such decoding may reveal representations more widely distributed than previously suspected. ...
As noted for simulations, this approach was unable to reveal code direction. Results for whole-brain decoding with sparse regularization are shown in the top panel of Figure 6 . ...
doi:10.1523/jneurosci.0904-20.2020
fatcat:jfbep6erzva65pigjdjzehnaim
Decoding the Encoding of Functional Brain Networks: an fMRI Classification Comparison of Non-negative Matrix Factorization (NMF), Independent Component Analysis (ICA), and Sparse Coding Algorithms
[article]
2016
arXiv
pre-print
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frameworks for generating brain networks. ...
The assumptions of independence, positivity, and sparsity to encode task-related brain networks are compared; the resulting brain networks for different constraints are used as basis functions to encode ...
L1 Regularized Learning We can relax the L0-norm constraint over the coefficients x i by instead using a L1-norm regularization (Olshausen et al. (1996) ), which enforce x i (i = 1, ...m) to have a small ...
arXiv:1607.00435v1
fatcat:caxeab3hqfarxeaheqflmuy26y
A spatio-spectral approach for movement decoding from cortical and subcortical recordings in patients with Parkinson's disease
[article]
2021
bioRxiv
pre-print
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We found that movement decoding was successful using 100 ms time windows, epoch time that is well-suited for aDBS applications. ...
Most decoding pipelines for aDBS are based on single channel frequency domain features, neglecting spatial information available in multichannel recordings. ...
Engemann for suggesting the use of a generalized linear model with non-negative distributions. ...
doi:10.1101/2021.06.06.447145
fatcat:wlfpxe4slbbofccpm5xojp6xta
Computational Methods in Neuroengineering
2013
Computational and Mathematical Methods in Medicine
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The paper "A mixed L2 norm regularized HRF estimation method for rapid event-related fMRI experiments" by Y. ...
It is demonstrated that the technique significantly improves the classification accuracy in decoding brain tasks in a rapid four-category object classification experiment. ...
In addition, we would also like to express our appreciation to the editorial board members and publishing office of the journal for their help and support throughout the preparation of this special issue ...
doi:10.1155/2013/617347
pmid:23935703
pmcid:PMC3713316
fatcat:r7uyvxriyncblj4taru4g75d7y
Decoding the encoding of functional brain networks: An fMRI classification comparison of non-negative matrix factorization (NMF), independent component analysis (ICA), and sparse coding algorithms
2017
Journal of Neuroscience Methods
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The assumptions of independence, positivity, and sparsity to encode task-related brain networks are compared; the resulting brain networks within scan for different constraints are used as basis functions ...
The sparse coding algorithm of L1 Regularized Learning outperformed 4 variations of ICA (p<0.001) for predicting the task being performed within each scan using artifact-cleaned components. ...
L1 Regularized Learning We can relax the L0-norm constraint over the coefficients x i by instead using a L1-norm regularization (Olshausen et al., 1996) , which enforce x i (i = 1, .. . m) to have a small ...
doi:10.1016/j.jneumeth.2017.03.008
pmid:28322859
pmcid:PMC5507942
fatcat:ymwe4txaijae5iujxa2iffdawu
Bootstrapped Adaptive Threshold Selection for Statistical Model Selection and Estimation
[article]
2015
arXiv
pre-print
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In part to account for such sparsity, structured regularizers are incorporated into model fitting optimization. ...
Finally, we apply this method to the task of decoding human speech production from ECoG recordings. ...
Furthermore, optimizing model performance is important for decoding brain signals, such as for the functioning of brain-machine interfaces. ...
arXiv:1505.03511v1
fatcat:zrapa2hzszeh3izl4b4dqzkkoy
A regularized discriminative framework for EEG analysis with application to brain–computer interface
2010
NeuroImage
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Keywords: Brain-computer interface Discriminative learning Regularization Group-lasso Spatio-temporal factorization Dual spectral norm Trace norm Convex optimization P300 speller Discriminative modeling ...
Farquhar et al. (2006) proposed to learn all the above coefficients 11 jointly with the hinge loss and the Frobenius norm regularization for coefficients {w j } j = 1 J , {B j } j = 1 J , and {β j } j ...
Acknowledgments This work was supported in part by grants of Japan Society for the Promotion of Science (JSPS) through the Global COE program (Computationism as a Foundation for the Sciences), Bundesministerium ...
doi:10.1016/j.neuroimage.2009.07.045
pmid:19646534
fatcat:tyirktmuz5de7bkejfwyb5lvey
Adaptive neural network classifier for decoding MEG signals
2019
NeuroImage
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We introduce two Convolutional Neural Network (CNN) classifiers optimized for inferring brain states from magnetoencephalographic (MEG) measurements. ...
The proposed networks outperform traditional classifiers as well as more complex neural networks when decoding evoked and induced responses to different stimuli across subjects. ...
Today, large-scale normative data sets of functional brain data are becoming increasingly available, alleviating this problem. ...
doi:10.1016/j.neuroimage.2019.04.068
pmid:31059799
pmcid:PMC6609925
fatcat:orsoefql25hm7fexgqtakpki6y
Filtrage vaste marge pour l'étiquetage séquentiel à noyaux de signaux
[article]
2010
arXiv
pre-print
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We derive algorithms to solve the optimization problem and we discuss different filter regularizations for automated scaling or selection of channels. ...
For that, we propose to jointly learn a SVM sample classifier with a temporal filtering of the channels. ...
For that, we can use a ℓ 1 − ℓ 2 mixed-norm as a regularizer : Ω 1−2 (F ) = d v f u F 2 u,v 1 2 = d v h ||F .,v || 2 (12)
Filtrage vaste marge with h(x) = x 1 2 the square root function. ...
arXiv:1007.0824v1
fatcat:nsgdojo4pzdebm2rwya33qhsoe
fMRI-based Decoding of Visual Information from Human Brain Activity: A Brief Review
2021
International Journal of Automation and Computing
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In addition, online resources and open research problems on brain pattern analysis are also provided for the convenience of future research. ...
In this paper, we mainly provide a comprehensive and up-to-date review of machine learning methods for analyzing neural activities with the following three aspects, i.e., brain image functional alignment ...
[44] proposed a logistic regression-based method as well as a combination of and -norm regularizations to select discriminant brain regions across multiple conditions or groups. Grosenick et al. ...
doi:10.1007/s11633-020-1263-y
fatcat:kwls2cvw4zgd5dti5d54uy6pgi
Decoding magnetoencephalographic rhythmic activity using spectrospatial information
2013
NeuroImage
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We propose a new data-driven decoding method called Spectral Linear Discriminant Analysis (Spectral LDA) for the analysis of magnetoencephalography (MEG). ...
Furthermore, the model allows the oscillation frequencies to be different for each such state. ...
Here, we constructed a brain decoding system for MEG with the explicit goal of providing an easily interpretable decoder, as well as a general-purpose decoding toolbox for neuroscientific research. ...
doi:10.1016/j.neuroimage.2013.07.026
pmid:23872494
fatcat:2f6cjzgrwbftzemfe3dgx6bvou
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