scholarly journals Voxel selection framework based on meta‐heuristic search and mutual information for brain decoding

2019 ◽  
Vol 29 (4) ◽  
pp. 663-676
Author(s):  
Osama Hourani ◽  
Nasrollah Moghadam Charkari ◽  
Saeed Jalili
Keyword(s):  
Mutual Information ◽  
Heuristic Search ◽  
Brain Decoding ◽  
Selection Framework ◽  
Voxel Selection

Effect of voxel selection on temporal mesh model for brain decoding

2016 ◽  
Author(s):  
Arman Afrasiyabi ◽  
Itir Onal ◽  
Fatos T. Yarman Vural
Keyword(s):  
Brain Decoding ◽  
Mesh Model ◽  
Voxel Selection

Voxel Selection Framework in Multi-Voxel Pattern Analysis of fMRI Data for Prediction of Neural Response to Visual Stimuli

2014 ◽  
Vol 33 (4) ◽  
pp. 925-934 ◽  
Author(s):  
Chun-An Chou ◽  
Kittipat Kampa ◽  
Sonya H. Mehta ◽  
Rosalia F. Tungaraza ◽  
W. Art Chaovalitwongse ◽  
...  
Keyword(s):  
Pattern Analysis ◽  
Visual Stimuli ◽  
Neural Response ◽  
Fmri Data ◽  
Selection Framework ◽  
Voxel Selection

A decomposition model and voxel selection framework for fMRI analysis to predict neural response of visual stimuli

2018 ◽  
Vol 63 (2) ◽  
pp. 163-175
Author(s):  
Savita V. Raut ◽  
Dinkar M. Yadav
Keyword(s):  
Signal Analysis ◽  
Geometric Mean ◽  
Frequency Component ◽  
Neural Response ◽  
Fmri Signal ◽  
Decomposition Models ◽  
Selection Framework ◽  
Fmri Analysis ◽  
Comparative Results ◽  
Voxel Selection

AbstractThis paper presents an fMRI signal analysis methodology using geometric mean curve decomposition (GMCD) and mutual information-based voxel selection framework. Previously, the fMRI signal analysis has been conducted using empirical mean curve decomposition (EMCD) model and voxel selection on raw fMRI signal. The erstwhile methodology loses frequency component, while the latter methodology suffers from signal redundancy. Both challenges are addressed by our methodology in which the frequency component is considered by decomposing the raw fMRI signal using geometric mean rather than arithmetic mean and the voxels are selected from EMCD signal using GMCD components, rather than raw fMRI signal. The proposed methodologies are adopted for predicting the neural response. Experimentations are conducted in the openly available fMRI data of six subjects, and comparisons are made with existing decomposition models and voxel selection frameworks. Subsequently, the effect of degree of selected voxels and the selection constraints are analyzed. The comparative results and the analysis demonstrate the superiority and the reliability of the proposed methodology.

scholarly journals Discovering dependencies with reliable mutual information

2020 ◽  
Vol 62 (11) ◽  
pp. 4223-4253
Author(s):  
Panagiotis Mandros ◽  
Mario Boley ◽  
Jilles Vreeken
Keyword(s):  
Mutual Information ◽  
Heuristic Search ◽  
Empirical Evaluation ◽  
Functional Dependencies ◽  
Greedy Search ◽  
Worst Case ◽  
Information Theoretic ◽  
Information Score ◽  
Heuristic Search Methods ◽  
Target Attributes

Abstract We consider the task of discovering functional dependencies in data for target attributes of interest. To solve it, we have to answer two questions: How do we quantify the dependency in a model-agnostic and interpretable way as well as reliably against sample size and dimensionality biases? How can we efficiently discover the exact or $$\alpha $$ α -approximate top-k dependencies? We address the first question by adopting information-theoretic notions. Specifically, we consider the mutual information score, for which we propose a reliable estimator that enables robust optimization in high-dimensional data. To address the second question, we then systematically explore the algorithmic implications of using this measure for optimization. We show the problem is NP-hard and justify worst-case exponential-time as well as heuristic search methods. We propose two bounding functions for the estimator, which we use as pruning criteria in branch-and-bound search to efficiently mine dependencies with approximation guarantees. Empirical evaluation shows that the derived estimator has desirable statistical properties, the bounding functions lead to effective exact and greedy search algorithms, and when combined, qualitative experiments show the framework indeed discovers highly informative dependencies.

Heuristic search for maximum of mutual information

2011 ◽  
Author(s):  
Ali Ghaffari Nejad ◽  
Ahmad Ayatollahi
Keyword(s):  
Mutual Information ◽  
Heuristic Search

The maximum mutual information without coding for binary quantum-state signals

1998 ◽  
Vol 45 (2) ◽  
pp. 269-282 ◽  
Author(s):  
MASAO OSAKI, OSAMU HIROTA MASASHI BAN
Keyword(s):  
Mutual Information ◽  
Quantum State ◽  
Maximum Mutual Information

A mutual information‐based likelihood function for particle filter flood extent assimilation

2021 ◽  
Author(s):  
Antara Dasgupta ◽  
Renaud Hostache ◽  
RAAJ Ramasankaran ◽  
Guy J.‐P Schumann ◽  
Stefania Grimaldi ◽  
...  
Keyword(s):  
Mutual Information ◽  
Particle Filter ◽  
Likelihood Function

A portfolio selection framework for choice under conflict

1995 ◽  
Author(s):  
Chen-Fu Chien ◽  
Francois Sainfort
Keyword(s):  
Portfolio Selection ◽  
Selection Framework
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