EEG signal based brain stimulation model to detect epileptic neurological disorders

Haewon Byeon , Udit Mahajan , Ashish Kumar , V. Rama Krishna , Mukesh Soni , Monika Bansal
{"title":"EEG signal based brain stimulation model to detect epileptic neurological disorders","authors":"Haewon Byeon ,&nbsp;Udit Mahajan ,&nbsp;Ashish Kumar ,&nbsp;V. Rama Krishna ,&nbsp;Mukesh Soni ,&nbsp;Monika Bansal","doi":"10.1016/j.neuri.2025.100186","DOIUrl":null,"url":null,"abstract":"<div><div><strong>Background:</strong> Manual visual inspection and analysis of electroencephalogram (EEG) signals of patients are susceptible to the subjective influence of doctors. The introduction of GA-PSO improved the categorization accuracy of both the EP (Evoked potential) and normal groups by automatically screening and optimizing the best feature combination of brain networks. Therefore, selecting effective EEG features for automatic recognition of EP is particularly important for Neuroscience.</div><div><strong>New method:</strong> A phase synchronization index (PSI) brain stimulation is constructed from multi-channel EEG signals, extracting 15 topological features from the perspectives of network nodes and structural functions. In order to optimize and screen feature combinations in both single and cross-frequency bands, the GA-PSO algorithm is utilized as a feature selection tool for choosing epileptic EEG network features.</div><div><strong>Result:</strong> Feature combinations are made both within and between bands, and the optimal feature mix is found using the PSO and GA-PSO algorithms. The study found that the GA-PSO algorithm outperformed the PSO algorithm, achieving a higher EP recognition accuracy of 0.9335 under cross-frequency band conditions.</div><div><strong>Comparison with existing methods:</strong> The results indicate that the introduction of the genetic algorithm enables the GA-PSO algorithm to maintain population diversity and avoid premature convergence to local optima, thereby enhancing the search capabilities of the population.</div><div><strong>Conclusion:</strong> Based on the findings, topological aspects provide a good way to describe the anomalies in the brain networks of epileptic patients and enhance the classification accuracy through combination, which provides help for pathological research and clinical diagnosis of epilepsy.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 1","pages":"Article 100186"},"PeriodicalIF":0.0000,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neuroscience informatics","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2772528625000019","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Background: Manual visual inspection and analysis of electroencephalogram (EEG) signals of patients are susceptible to the subjective influence of doctors. The introduction of GA-PSO improved the categorization accuracy of both the EP (Evoked potential) and normal groups by automatically screening and optimizing the best feature combination of brain networks. Therefore, selecting effective EEG features for automatic recognition of EP is particularly important for Neuroscience.
New method: A phase synchronization index (PSI) brain stimulation is constructed from multi-channel EEG signals, extracting 15 topological features from the perspectives of network nodes and structural functions. In order to optimize and screen feature combinations in both single and cross-frequency bands, the GA-PSO algorithm is utilized as a feature selection tool for choosing epileptic EEG network features.
Result: Feature combinations are made both within and between bands, and the optimal feature mix is found using the PSO and GA-PSO algorithms. The study found that the GA-PSO algorithm outperformed the PSO algorithm, achieving a higher EP recognition accuracy of 0.9335 under cross-frequency band conditions.
Comparison with existing methods: The results indicate that the introduction of the genetic algorithm enables the GA-PSO algorithm to maintain population diversity and avoid premature convergence to local optima, thereby enhancing the search capabilities of the population.
Conclusion: Based on the findings, topological aspects provide a good way to describe the anomalies in the brain networks of epileptic patients and enhance the classification accuracy through combination, which provides help for pathological research and clinical diagnosis of epilepsy.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
求助全文
约1分钟内获得全文 去求助
来源期刊
Neuroscience informatics
Neuroscience informatics Surgery, Radiology and Imaging, Information Systems, Neurology, Artificial Intelligence, Computer Science Applications, Signal Processing, Critical Care and Intensive Care Medicine, Health Informatics, Clinical Neurology, Pathology and Medical Technology
自引率
0.00%
发文量
0
审稿时长
57 days
期刊最新文献
Analyzing infant cry to detect birth asphyxia using a hybrid CNN and feature extraction approach Editorial Board Contents Neuroscience-informed nomogram model for early prediction of cognitive impairment in Parkinson's disease Automated classification of epileptic seizures using modified one-dimensional convolution neural network based on empirical mode decomposition with high accuracy
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1