scholarly journals Multicompare Tests of the Performance of Different Metaheuristics in EEG Dipole Source Localization

2014 ◽  
Vol 2014 ◽  
pp. 1-9
Author(s):  
Diana Irazú Escalona-Vargas ◽  
Ivan Lopez-Arevalo ◽  
David Gutiérrez
Keyword(s):  
Source Localization ◽  
Large Scale ◽  
Optimization Problem ◽  
Statistical Tests ◽  
Computational Cost ◽  
Fixed Number ◽  
Dipole Source ◽  
Operational Parameters ◽  
Metaheuristic Methods ◽  
Eeg Source Localization

We study the use of nonparametric multicompare statistical tests on the performance of simulated annealing (SA), genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE), when used for electroencephalographic (EEG) source localization. Such task can be posed as an optimization problem for which the referred metaheuristic methods are well suited. Hence, we evaluate the localization’s performance in terms of metaheuristics’ operational parameters and for a fixed number of evaluations of the objective function. In this way, we are able to link the efficiency of the metaheuristics with a common measure of computational cost. Our results did not show significant differences in the metaheuristics’ performance for the case of single source localization. In case of localizing two correlated sources, we found that PSO (ring and tree topologies) and DE performed the worst, then they should not be considered in large-scale EEG source localization problems. Overall, the multicompare tests allowed to demonstrate the little effect that the selection of a particular metaheuristic and the variations in their operational parameters have in this optimization problem.

scholarly journals Locating the Source of Diffusion in Complex Networks via Gaussian-Based Localization and Deduction

2019 ◽  
Vol 9 (18) ◽  
pp. 3758 ◽  
Author(s):  
Xiang Li ◽  
Xiaojie Wang ◽  
Chengli Zhao ◽  
Xue Zhang ◽  
Dongyun Yi
Keyword(s):  
Complex Networks ◽  
Source Localization ◽  
Information Diffusion ◽  
Large Scale ◽  
Computational Cost ◽  
Likelihood Estimation ◽  
Power Grids ◽  
Optimization Approach ◽  
Localization Accuracy ◽  
Diffusion Source

Locating the source that undergoes a diffusion-like process is a fundamental and challenging problem in complex network, which can help inhibit the outbreak of epidemics among humans, suppress the spread of rumors on the Internet, prevent cascading failures of power grids, etc. However, our ability to accurately locate the diffusion source is strictly limited by incomplete information of nodes and inevitable randomness of diffusion process. In this paper, we propose an efficient optimization approach via maximum likelihood estimation to locate the diffusion source in complex networks with limited observations. By modeling the informed times of the observers, we derive an optimal source localization solution for arbitrary trees and then extend it to general graphs via proper approximations. The numerical analyses on synthetic networks and real networks all indicate that our method is superior to several benchmark methods in terms of the average localization accuracy, high-precision localization and approximate area localization. In addition, low computational cost enables our method to be widely applied for the source localization problem in large-scale networks. We believe that our work can provide valuable insights on the interplay between information diffusion and source localization in complex networks.

scholarly journals High-Resolution EEG Source Localization in Segmentation-Free Head Models Based on Finite-Difference Method and Matching Pursuit Algorithm

2021 ◽  
Vol 15 ◽  
Author(s):  
Takayoshi Moridera ◽  
Essam A. Rashed ◽  
Shogo Mizutani ◽  
Akimasa Hirata
Keyword(s):  
Inverse Problem ◽  
High Resolution ◽  
Finite Difference ◽  
Finite Difference Method ◽  
Source Localization ◽  
Difference Method ◽  
Computational Cost ◽  
Head Model ◽  
Eeg Source Localization ◽  
The Brain

Electroencephalogram (EEG) is a method to monitor electrophysiological activity on the scalp, which represents the macroscopic activity of the brain. However, it is challenging to identify EEG source regions inside the brain based on data measured by a scalp-attached network of electrodes. The accuracy of EEG source localization significantly depends on the type of head modeling and inverse problem solver. In this study, we adopted different models with a resolution of 0.5 mm to account for thin tissues/fluids, such as the cerebrospinal fluid (CSF) and dura. In particular, a spatially dependent conductivity (segmentation-free) model created using deep learning was developed and used for more realist representation of electrical conductivity. We then adopted a multi-grid-based finite-difference method (FDM) for forward problem analysis and a sparse-based algorithm to solve the inverse problem. This enabled us to perform efficient source localization using high-resolution model with a reasonable computational cost. Results indicated that the abrupt spatial change in conductivity, inherent in conventional segmentation-based head models, may trigger source localization error accumulation. The accurate modeling of the CSF, whose conductivity is the highest in the head, was an important factor affecting localization accuracy. Moreover, computational experiments with different noise levels and electrode setups demonstrate the robustness of the proposed method with segmentation-free head model.

scholarly journals A Distributed Quantum-Behaved Particle Swarm Optimization Using Opposition-Based Learning on Spark for Large-Scale Optimization Problem

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1860
Author(s):  
Zhaojuan Zhang ◽  
Wanliang Wang ◽  
Gaofeng Pan
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Large Scale ◽  
Optimization Problem ◽  
Computational Cost ◽  
Particle Swarm ◽  
Large Scale Optimization ◽  
Swarm Optimization ◽  
Opposition Based Learning ◽  
Scale Optimization

In the era of big data, the size and complexity of the data are increasing especially for those stored in remote locations, and whose difficulty is further increased by the ongoing rapid accumulation of data scale. Real-world optimization problems present new challenges to traditional intelligent optimization algorithms since the traditional serial optimization algorithm has a high computational cost or even cannot deal with it when faced with large-scale distributed data. Responding to these challenges, a distributed cooperative evolutionary algorithm framework using Spark (SDCEA) is first proposed. The SDCEA can be applied to address the challenge due to insufficient computing resources. Second, a distributed quantum-behaved particle swarm optimization algorithm (SDQPSO) based on the SDCEA is proposed, where the opposition-based learning scheme is incorporated to initialize the population, and a parallel search is conducted on distributed spaces. Finally, the performance of the proposed SDQPSO is tested. In comparison with SPSO, SCLPSO, and SALCPSO, SDQPSO can not only improve the search efficiency but also search for a better optimum with almost the same computational cost for the large-scale distributed optimization problem. In conclusion, the proposed SDQPSO based on the SDCEA framework has high scalability, which can be applied to solve the large-scale optimization problem.

scholarly journals A Distributed Algorithm for Large-Scale Linearly Coupled Resource Allocation Problems with Selfish Agents

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Dian Yu ◽  
Tongyao Wang
Keyword(s):  
Distributed Algorithm ◽  
Large Scale ◽  
Pareto Front ◽  
Optimization Problem ◽  
Computational Cost ◽  
Descent Method ◽  
Resource Optimization ◽  
Linearly Constrained ◽  
Objective Value ◽  
Selfish Agents

A decentralized randomized coordinate descent method is proposed to solve a large-scale linearly constrained, separable resource optimization problem with selfish agent. This method has a cheap computational cost and can guarantee an improvement of selected objective function without jeopardizing the others in each iteration. The convergence rate is obtained using an alternative gap benchmark of objective value. Numerical simulations suggest that the algorithm will converge to a random point on the Pareto front.

scholarly journals decorate: differential epigenetic correlation test

2020 ◽  
Vol 36 (9) ◽  
pp. 2856-2861
Author(s):  
Gabriel E Hoffman ◽  
Jaroslav Bendl ◽  
Kiran Girdhar ◽  
Panos Roussos
Keyword(s):  
Large Scale ◽  
Statistical Tests ◽  
Computational Cost ◽  
R Package ◽  
Supplementary Information ◽  
Expression Data ◽  
Correlation Test ◽  
Disease Biology ◽  
Genome Wide ◽  
Insight Into

Abstract Motivation Identifying correlated epigenetic features and finding differences in correlation between individuals with disease compared to controls can give novel insight into disease biology. This framework has been successful in analysis of gene expression data, but application to epigenetic data has been limited by the computational cost, lack of scalable software and lack of robust statistical tests. Results Decorate, differential epigenetic correlation test, identifies correlated epigenetic features and finds clusters of features that are differentially correlated between two or more subsets of the data. The software scales to genome-wide datasets of epigenetic assays on hundreds of individuals. We apply decorate to four large-scale datasets of DNA methylation, ATAC-seq and histone modification ChIP-seq. Availability and implementation decorate R package is available from https://github.com/GabrielHoffman/decorate. Supplementary information Supplementary data are available at Bioinformatics online.

An inverse dipole source problem in inhomogeneous media: Application to the EEG source localization in neonates

2019 ◽  
Vol 27 (2) ◽  
pp. 255-281
Author(s):  
Marion Darbas ◽  
Mohamadou Malal Diallo ◽  
Abdellatif El Badia ◽  
Stephanie Lohrengel
Keyword(s):  
Source Localization ◽  
Numerical Experiments ◽  
Dirichlet Boundary ◽  
Stability Result ◽  
Uniqueness Result ◽  
Dipole Source ◽  
Inverse Source Problem ◽  
Varying Coefficients ◽  
Boundary Measurements ◽  
Eeg Source Localization

Abstract In this paper we present some aspects of an inverse source problem in an elliptic equation with varying coefficients, using partial Dirichlet boundary measurements. A uniqueness result is established for dipolar sources including their number. Additionally, assuming the number of dipoles known, a stability result is obtained and an efficient numerical identification method is developed. Finally, numerical experiments illustrate the effectiveness of the approach and a discussion is given on electroencephalography (EEG) in neonates.

A Metaheuristic Approach to Transmembrane Protein Assembly Using Limited Distance Restraints

2013 ◽  
Vol 701 ◽  
pp. 403-407
Author(s):  
Kanon Sujaree ◽  
Pornthep Sompornpisut
Keyword(s):  
Large Scale ◽  
Optimization Problem ◽  
Transmembrane Protein ◽  
Symmetry Transformation ◽  
Ant System ◽  
X Ray ◽  
Metaheuristic Methods ◽  
Distance Restraints ◽  
Scale Optimization ◽  
Atomic Coordinates

Metaheuristic methods have become a popular tool in solving large scale optimization problem for a variety of biological systems. In this report, we present Max-Min Ant System (MMAS), a class of swarm intelligence metaheuristics approach, in computing transmembrane helical arrangement of the homotetrameric protein, the potassium channel from Streptomyces iividans (KcsA). The MMAS algorithm was employed to solve transmembrane arrangement problems through the use of an objective penalty function based on distance-violated constraints. Assembly structures of the four inner helices of the KcsA channel were computed bythe construction of probability associated with a set of translational and rotational parameters and the four-fold symmetry transformation applied to the atomic coordinates of the rigid single helix. The MMAS parameters including the number of ants, the number of iteration, weight of pheromone, weight of heuristic information, and pheromone evaporation weight were examined. We demonstrated the effectiveness of the present approach, which can correctly generate native-like structure with root-mean square deviation (RMSD) below 3 Å with respect to the x-ray structure.

scholarly journals Evaluation of Mixed Deep Neural Networks for Reverberant Speech Enhancement

Biomimetics ◽  
2019 ◽  
Vol 5 (1) ◽  
pp. 1 ◽  
Author(s):  
Michelle Gutiérrez-Muñoz ◽  
Astryd González-Salazar ◽  
Marvin Coto-Jiménez
Keyword(s):  
Neural Networks ◽  
Short Term Memory ◽  
Computational Cost ◽  
Real Life ◽  
Fixed Number ◽  
Training Procedure ◽  
Statistical Validation ◽  
Significant Drop ◽  
Training Time ◽  
Important Solution

Speech signals are degraded in real-life environments, as a product of background noise or other factors. The processing of such signals for voice recognition and voice analysis systems presents important challenges. One of the conditions that make adverse quality difficult to handle in those systems is reverberation, produced by sound wave reflections that travel from the source to the microphone in multiple directions. To enhance signals in such adverse conditions, several deep learning-based methods have been proposed and proven to be effective. Recently, recurrent neural networks, especially those with long short-term memory (LSTM), have presented surprising results in tasks related to time-dependent processing of signals, such as speech. One of the most challenging aspects of LSTM networks is the high computational cost of the training procedure, which has limited extended experimentation in several cases. In this work, we present a proposal to evaluate the hybrid models of neural networks to learn different reverberation conditions without any previous information. The results show that some combinations of LSTM and perceptron layers produce good results in comparison to those from pure LSTM networks, given a fixed number of layers. The evaluation was made based on quality measurements of the signal’s spectrum, the training time of the networks, and statistical validation of results. In total, 120 artificial neural networks of eight different types were trained and compared. The results help to affirm the fact that hybrid networks represent an important solution for speech signal enhancement, given that reduction in training time is on the order of 30%, in processes that can normally take several days or weeks, depending on the amount of data. The results also present advantages in efficiency, but without a significant drop in quality.

Dipole source localization of epileptic discharges in EEG and MEG

2004 ◽  
Vol 1270 ◽  
pp. 56-60
Author(s):  
Hiroshi Otsubo ◽  
Ayako Ochi ◽  
Ryota Sakamoto ◽  
Koji Iida
Keyword(s):  
Source Localization ◽  
Dipole Source ◽  
Dipole Source Localization ◽  
Epileptic Discharges
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