Source Localization and Spectrum Analyzing of EEG in Stuttering State upon Dysfluent Utterances.

IF 1.6 4区 医学 Q3 CLINICAL NEUROLOGY Clinical EEG and Neuroscience Pub Date : 2024-05-01 Epub Date: 2023-01-10 DOI:10.1177/15500594221150638
Masoumeh Bayat, Reza Boostani, Malihe Sabeti, Fariba Yadegari, Mohammadreza Pirmoradi, K S Rao, Mohammad Nami
{"title":"Source Localization and Spectrum Analyzing of EEG in Stuttering State upon Dysfluent Utterances.","authors":"Masoumeh Bayat, Reza Boostani, Malihe Sabeti, Fariba Yadegari, Mohammadreza Pirmoradi, K S Rao, Mohammad Nami","doi":"10.1177/15500594221150638","DOIUrl":null,"url":null,"abstract":"<p><p><b>Purpose:</b> The present study which addressed adults who stutter (AWS) attempted to investigate power spectral dynamics in the stuttering state by answering the questions using quantitative electroencephalography (qEEG). <b>Method:</b> A 64-channel electroencephalography (EEG) setup was used for data acquisition at 20 AWS. Since the speech, especially stuttering, causes significant noise in the EEG, 2 conditions of speech preparation (SP) and imagined speech (IS) were considered. EEG signals were decomposed into 6 bands. The corresponding sources were localized using the standard low-resolution electromagnetic tomography (sLORETA) tool in both fluent and dysfluent states. <b>Results:</b> Significant differences were noted after analyzing the time-locked EEG signals in fluent and dysfluent utterances. Consistent with previous studies, poor alpha and beta suppression in SP and IS conditions were localized in the left frontotemporal areas in a dysfluent state. This was partly true for the right frontal regions. In the theta range, disfluency was concurrence with increased activation in the left and right motor areas. Increased delta power in the left and right motor areas as well as increased beta2 power over left parietal regions was notable EEG features upon fluent speech. <b>Conclusion:</b> Based on the present findings and those of earlier studies, explaining the neural circuitries involved in stuttering probably requires an examination of the entire frequency spectrum involved in speech.</p>","PeriodicalId":10682,"journal":{"name":"Clinical EEG and Neuroscience","volume":" ","pages":"371-383"},"PeriodicalIF":1.6000,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical EEG and Neuroscience","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1177/15500594221150638","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/1/10 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Purpose: The present study which addressed adults who stutter (AWS) attempted to investigate power spectral dynamics in the stuttering state by answering the questions using quantitative electroencephalography (qEEG). Method: A 64-channel electroencephalography (EEG) setup was used for data acquisition at 20 AWS. Since the speech, especially stuttering, causes significant noise in the EEG, 2 conditions of speech preparation (SP) and imagined speech (IS) were considered. EEG signals were decomposed into 6 bands. The corresponding sources were localized using the standard low-resolution electromagnetic tomography (sLORETA) tool in both fluent and dysfluent states. Results: Significant differences were noted after analyzing the time-locked EEG signals in fluent and dysfluent utterances. Consistent with previous studies, poor alpha and beta suppression in SP and IS conditions were localized in the left frontotemporal areas in a dysfluent state. This was partly true for the right frontal regions. In the theta range, disfluency was concurrence with increased activation in the left and right motor areas. Increased delta power in the left and right motor areas as well as increased beta2 power over left parietal regions was notable EEG features upon fluent speech. Conclusion: Based on the present findings and those of earlier studies, explaining the neural circuitries involved in stuttering probably requires an examination of the entire frequency spectrum involved in speech.

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
口吃状态下的脑电波源定位和频谱分析。
目的:本研究以口吃成人(AWS)为对象,试图通过定量脑电图(qEEG)来回答口吃状态下的功率谱动态问题。研究方法:使用 64 通道脑电图(EEG)装置采集 20 名口吃者的数据。由于语音(尤其是口吃)会在脑电图中产生大量噪声,因此考虑了语音准备(SP)和想象语音(IS)两种情况。脑电信号被分解成 6 个波段。使用标准低分辨率电磁断层扫描(sLORETA)工具对流利和不流利状态下的相应信号源进行定位。结果:对流利语和不流利语的时间锁定脑电信号进行分析后,发现两者之间存在显著差异。与之前的研究结果一致,SP 和 IS 条件下较差的阿尔法和贝塔抑制都集中在流利语障碍状态下的左侧额颞叶区域。右额叶区的情况也部分如此。在θ范围内,不流畅与左右运动区的激活增加同时出现。左侧和右侧运动区的 delta 功率增加以及左侧顶叶区的β2 功率增加是流利说话时的显著脑电图特征。结论:根据目前的研究结果和之前的研究结果,要解释口吃所涉及的神经回路,可能需要检查言语所涉及的整个频谱。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Clinical EEG and Neuroscience
Clinical EEG and Neuroscience 医学-临床神经学
CiteScore
5.20
自引率
5.00%
发文量
66
审稿时长
>12 weeks
期刊介绍: Clinical EEG and Neuroscience conveys clinically relevant research and development in electroencephalography and neuroscience. Original articles on any aspect of clinical neurophysiology or related work in allied fields are invited for publication.
期刊最新文献
Ikelos-RWA. Validation of an Automatic Tool to Quantify REM Sleep Without Atonia. Age-dependent Electroencephalogram Characteristics During Different Levels of Anesthetic Depth. The Clinical Utility of Finding Unexpected Subclinical Spikes Detected by High-Density EEG During Neurodiagnostic Investigations Comparative Analysis of LORETA Z Score Neurofeedback and Cognitive Rehabilitation on Quality of Life and Response Inhibition in Individuals with Opioid Addiction Deep Learning-Based Artificial Intelligence Can Differentiate Treatment-Resistant and Responsive Depression Cases 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