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Non-invasive EEG source localization using particle swarm optimization: A clinical experiment

Y. Shirvany, F. Edelvik, S. Jakobsson, A. Hedstrom, Q. Mahmood, A. Chodorowski, M. Persson
2012 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society  
The non-invasive EEG source analysis methods localized the somatosensory cortex area where our clinical expert expected the received SEPs.  ...  In this paper a modified particle swarm optimization (MPSO) method is applied to a real EEG data, i.e., a somatosensory evoked potentials (SEPs) measured from a healthy subject, to solve the EEG source  ...  Simon Bergstrand from the Sahlgrenska University Hospital for providing us the hospital measurement resources to measure the EEG and MRI data.  ... 
doi:10.1109/embc.2012.6347418 pmid:23367353 dblp:conf/embc/ShirvanyEJHMCP12 fatcat:f6ykostkkrbqtiaswtqvnlc2bm

SVM classification model in depression recognition based on mutation PSO parameter optimization

Ming Zhang, Shengfu Lu, Mi Li, Qian zhai, Jia Zhou, Xiaofeng Lu, Jiying Xu, Jia Xue, Ning Zhong, J.Q. Cheng, H.L. Moffitt, I. Kim (+2 others)
2017 BIO Web of Conferences  
To address on the problem that particle swarm optimization (PSO) algorithm easily trap in local optima, we propose a feedback mutation PSO algorithm (FBPSO) to balance the local search and global exploration  ...  In this paper, a method of depression recognition based on SVM and particle swarm optimization algorithm mutation is proposed.  ...  What's more, the fMRI is a non-invasive and no radiation exposing auxiliary diagnostic method. However, the fMRI can't achieve the purpose of diagnosis depression. The eyes are the source of emotion.  ... 
doi:10.1051/bioconf/20170801037 fatcat:g45dh6vmyvabvau6wnidekwhna

Multichannel Optimization with Hybrid Spectral-entropy Markers for Gender Identification Enhancement of Emotional-based EEGs

Noor Kamal Al-Qazzaz, Mohannad K. Sabir, Sawal Hamid Bin Mohd Ali, Siti Anom Ahmad, Karl Grammer
2021 IEEE Access  
EEGs. 1) Binary Particle Swarm Optimization (BPSO) Binary particle swarm optimization (BPSO) is a binary version of particle swarm optimization (PSO) that was created by Kennedy and Eberhart in 1995  ...  17F/13M Anger 1.867±0.01 Happiness 2.249±0.052 Sadness 2.897±0.842 TABLE 4 . 4 The Binary Particle Swarm Optimization (BPSO) pseudocode.Algorithm 2: The binary particle swarm optimization  ... 
doi:10.1109/access.2021.3096430 fatcat:katwwyu6lngzxc4d7zb4mrqupi

An Enhanced Ant Colony Optimization Mechanism for the Classification of Depressive Disorders

Abed Saif Alghawli, Ahmed I. Taloba, Heng Liu
2022 Computational Intelligence and Neuroscience  
In respect of classifications efficiency and frequency of features extracted, the performance of the IACO method was linked to that of regular ACO, particle swarm optimization (PSO), and genetic algorithm  ...  The validation was performed using a nested cross-validation (CV) approach to produce nearly reliable estimates of classification error.  ...  SVM with a selection of features extracted was used to classify 46 BD and 55 MDD cases. e genetic algorithm (GA), particle swarm optimization (PSO), and ACO and IACO algorithms were used in the FS process  ... 
doi:10.1155/2022/1332664 pmid:35800708 pmcid:PMC9256370 fatcat:b2hvdgheovd3vhfey5uj5i6nw4

Advances in neuro diagnostics based on microwave technology, transcranial magnetic stimulation and EEG source localization

Mikael Persson, Tomas Mckelvey, Andreas Fhager, Yazdan Shirvany, Artur Chodorowski, Qaiser Mahmood, Fredrik Edelevik, Magnus Thordstein, Anders Hedstrom, Mikael Elam
2011 2011 XXXth URSI General Assembly and Scientific Symposium  
Conclusions We have presented a novel technique for EEG-based source localization based on a combination of adaptive FEM and particle swarm optimization.  ...  Here, we present a novel approach for source localization that combines an adaptive FEM solver with a particle swarm optimization (PSO) algorithm. A.  ... 
doi:10.1109/ursigass.2011.6123736 fatcat:f7stnmtukbfdzdubswbyzowuwi

Lung Sounds Signal Separation Model of Medical Monitoring Based on Wireless Sensor Network

Beibei Dong, Bing Han, Jingjing Yang, Wei Peng
2015 International Journal of Multimedia and Ubiquitous Engineering  
First, using the optimization strategy of the flying speed and the effect between particles to two-way optimization for particle swarm optimization algorithm (PSOA), and then applied it to the blind source  ...  , did coverage optimization for wireless sensor network by using the improved algorithm, to expand the scope of wireless data transmission.  ...  To provide new non-invasive diagnostic method for clinic, we need to adopt new theories and methods to extract and identify the acoustic information of human organs and then find out the rules for clinical  ... 
doi:10.14257/ijmue.2015.10.12.11 fatcat:yabarpxgp5helbuneh6nnrohaq

Bagging of EEG Signals for Brain Computer Interface

K. Akilandeswari, G. M. Nasira
2014 2014 World Congress on Computing and Communication Technologies  
Feature selection through Particle Swarm Optimization (PSO) is proposed. Classification of the features is achieved through Bagging and decision tree classifiers.  ...  A Brain-Computer Interface (BCI) is a communication system which uses cerebral activity to control external devices or computers.  ...  Particle Swarm Optimization (PSO) PSO is used to find the optimal feature subset of the Eigen features extracted using PCA.  ... 
doi:10.1109/wccct.2014.42 fatcat:ra4zarcxanbvbbd5vcy62zm7qi

Research on Brain Signals via Artificial Neural Network and Swarm Intelligence Algorithms

Sema YİLDİRİM, Hasan Erdinç KOÇER, A.hakan EKMEKCİ
2019 International Journal of Applied Mathematics Electronics and Computers  
In order to avoid this problem and to obtain a better classifier, we proposed an ANNs and Swarm Intelligence (SI) method where Artificial Bee Colony and Particle Swarm Optimization algorithms were operated  ...  In addition to this, this study showed that the SI-MLPNN algorithm can be used on non-linear and highly complex EEG data.  ...  Acknowledgements We would like to thank to the doctors who shared their knowledge and experience and to the Department of Neurology of Selcuk University Hospital (Non-Invasive Clinical Research Ethics  ... 
doi:10.18100/ijamec.475090 fatcat:7an3sjyq6vf2xo34xw52nl7jsy

A systematic review of EEG source localization techniques and their applications on diagnosis of brain abnormalities [article]

Shiva Asadzadeh, Tohid Yousefi Rezaii, Soosan Beheshti, Azra Delpak,, Saeed Meshgini
2019 arXiv   pre-print
Electroencephalography (EEG) is a popular non-invasive electrophysiological technique of relatively very high time resolution which is used to measure electric potential of brain neural activity.  ...  research using EEG source localization methods.  ...  Schoener, "Age-dependent decline of attention particle swarm optimization: A clinical experiment," in 2012 Annual deficit hyperactivity disorder," (in eng), Am J Psychiatry, vol. 153,  ... 
arXiv:1910.07980v1 fatcat:rxc6u3d3tvhqpnw6lkt22o44tu

State-of-the Art Optimal Multilevel Thresholding Methods for Brain MR Image Analysis

Akankshya Das, Sanjay Agrawal, Leena Samantaray, Rutuparna Panda, Ajith Abraham
2020 Revue d'intelligence artificielle : Revue des Sciences et Technologies de l'Information  
The brain MR image analysis is a primary non-invasive component to detect any abnormality in the brain. It is a very important application in the field of medical image processing.  ...  In this paper, an attempt is made to present a comprehensive review on the recent advancements in the area of brain MR image segmentation using optimal multilevel thresholding.  ...  Clinical usage Brain MR Image analysis is a non-invasive exploration of the clinical details of the brain.  ... 
doi:10.18280/ria.340302 fatcat:2kiajolrzrfqfa4nwc5uwd7mcu

Parameter estimation and identifiability in a neural population model for electro-cortical activity [article]

Agus Hartoyo, Peter J. Cadusch, David T. J. Liley, Damien G. Hicks
2018 biorxiv/medrxiv   pre-print
Here we perform unbiased fits of a 22-parameter neural population model to EEG data from 82 individuals, using both particle swarm optimization and Markov chain Monte Carlo sampling.  ...  AbstractElectroencephalography (EEG) provides a non-invasive measure of brain electrical activity.  ...  We fit the model to the EEG spectrum from each of 82 subjects using both a particle swarm optimization and Markov chain Monte Carlo method.  ... 
doi:10.1101/492504 fatcat:22jaht2qdbb2re3zlyiym75oni

Incorporating priors for EEG source imaging and connectivity analysis

Xu Lei, Taoyu Wu, Pedro A. Valdes-Sosa
2015 Frontiers in Neuroscience  
Electroencephalography source imaging (ESI) is a useful technique to localize the generators from a given scalp electric measurement and to investigate the temporal dynamics of the large-scale neural circuits  ...  We conclude that combining EEG source imaging with other complementary modalities is a promising approach toward the study of brain networks in cognitive and clinical neurosciences.  ...  The particle-coded space is compressed by the evolution of particle swarm optimization algorithm .  ... 
doi:10.3389/fnins.2015.00284 pmid:26347599 pmcid:PMC4539512 fatcat:dslnmr32h5cnzfz5u23eqi37xa

Detection of gait initiation Failure in Parkinson's disease based on wavelet transform and Support Vector Machine

Quynh Tran Ly, A.M. Ardi Handojoseno, Moran Gilat, Rifai Chai, Kaylena A. Ehgoetz Martens, Matthew Georgiades, Ganesh R. Naik, Yvonne Tran, Simon J.G. Lewis, Hung T Nguyen
2017 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)  
These results suggest that our proposed methodology is a promising non-invasive approach to improve GIF detection in PD and FOG.  ...  Studies investigating the effects of GIF on brain activity using EEG offer the potential to study such behavior.  ...  These results suggest that our proposed methodology is a promising non-invasive approach for improvement of GIF detection. II. METHODS A.  ... 
doi:10.1109/embc.2017.8037500 pmid:29060541 dblp:conf/embc/LyHGCMGNTL017a fatcat:hmlyn2gvb5hqdhgixc5c737h7u

A Comprehensive Review on Critical Issues and Possible Solutions of Motor Imagery Based Electroencephalography Brain-Computer Interface

Amardeep Singh, Ali Abdul Hussain, Sunil Lal, Hans W. Guesgen
2021 Sensors  
This paper provides a comprehensive review of the electroencephalogram (EEG) based MI-BCI system.  ...  Every year, a significant number of publications that are related to new improvements, challenges, and breakthrough in MI-BCI are made.  ...  [130] optimized SVM classifier's kernel and penalty parameters through a particle swarm optimization algorithm to obtain optimal CSP features. Furthermore, Costa et al.  ... 
doi:10.3390/s21062173 pmid:33804611 pmcid:PMC8003721 fatcat:xgqftpxyajfgtny4mml77k5kfy

Monitoring, signal analysis, and control of epileptic seizures: A paradigm in brain research

J. Echauz, G. Georgoulas, O. Smart, A. Gardner, H. Firpi, B. Litt, G.J. Vachtsevanos
2007 2007 Mediterranean Conference on Control & Automation  
We review in this paper, work in the area of monitoring and EEG signal analysis aimed to detect/predict seizures and we propose a closed-loop control scheme to stimulate electrically the source of epileptiform  ...  This paper introduces a framework for addressing neurological disorders and specifically epilepsy.  ...  The method uses Particle Swarm Optimization (PSO) to train an artificial neural network [19] .  ... 
doi:10.1109/med.2007.4433785 fatcat:dcmlcld25beerfmu6lv3n25w3q
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