[HTML][HTML] On the interpretation of weight vectors of linear models in multivariate neuroimaging
The increase in spatiotemporal resolution of neuroimaging devices is accompanied by a
trend towards more powerful multivariate analysis methods. Often it is desired to interpret the …
trend towards more powerful multivariate analysis methods. Often it is desired to interpret the …
MEG-based decoding of the spatiotemporal dynamics of visual category perception
ME van de Nieuwenhuijzen, AR Backus… - Neuroimage, 2013 - Elsevier
Visual processing is a complex task which is best investigated using sensitive multivariate
analysis methods that can capture representation-specific brain activity over both time and …
analysis methods that can capture representation-specific brain activity over both time and …
Localizing and comparing weight maps generated from linear kernel machine learning models
J Schrouff, J Cremers, G Garraux… - … Workshop on Pattern …, 2013 - ieeexplore.ieee.org
Recently, machine learning models have been applied to neuroimaging data, allowing to
make predictions about a variable of interest based on the pattern of activation or anatomy …
make predictions about a variable of interest based on the pattern of activation or anatomy …
Interpretability of multivariate brain maps in linear brain decoding: Definition, and heuristic quantification in multivariate analysis of MEG time-locked effects
Brain decoding is a popular multivariate approach for hypothesis testing in neuroimaging.
Linear classifiers are widely employed in the brain decoding paradigm to discriminate …
Linear classifiers are widely employed in the brain decoding paradigm to discriminate …
Using within-subject pattern classification to understand lifespan age differences in oscillatory mechanisms of working memory selection and maintenance
In lifespan studies, large within-group heterogeneity with regard to behavioral and neuronal
data is observed. This casts doubt on the validity of group-statistics-based approaches to …
data is observed. This casts doubt on the validity of group-statistics-based approaches to …
Group-level spatio-temporal pattern recovery in MEG decoding using multi-task joint feature learning
Background The use of machine learning models to discriminate between patterns of neural
activity has become in recent years a standard analysis approach in neuroimaging studies …
activity has become in recent years a standard analysis approach in neuroimaging studies …
Eigenanatomy: Sparse dimensionality reduction for multi-modal medical image analysis
Rigorous statistical analysis of multimodal imaging datasets is challenging. Mass-univariate
methods for extracting correlations between image voxels and outcome measurements are …
methods for extracting correlations between image voxels and outcome measurements are …
A machine learning perspective on repeated measures
J Karch - 2016 - edoc.hu-berlin.de
Repeated measures obtained from multiple individuals are of crucial importance for
developmental research. Examples of repeated measures obtained from multiple individuals …
developmental research. Examples of repeated measures obtained from multiple individuals …
A periodic spatio-spectral filter for event-related potentials
With respect to single trial detection of event-related potentials (ERPs), spatial and spectral
filters are two of the most commonly used pre-processing techniques for signal …
filters are two of the most commonly used pre-processing techniques for signal …
Fdr-hs: An empirical bayesian identification of heterogenous features in neuroimage analysis
Recent studies found that in voxel-based neuroimage analysis, detecting and differentiating
“procedural bias” that are introduced during the preprocessing steps from lesion features …
“procedural bias” that are introduced during the preprocessing steps from lesion features …