预测双耳节拍对工作记忆的影响

IF 1.6 4区 医学 Q4 NEUROSCIENCES
Neuroreport Pub Date : 2024-12-04 Epub Date: 2024-09-30 DOI:10.1097/WNR.0000000000002101
Ahmad Zahid Rao, Muhammad Danish Mujib, Saad Ahmed Qazi, Ahmad O Alokaily, Ayesha Ikhlaq, Eraj Humayun Mirza, Ahmed Ali Aldohbeyb, Muhammad Abul Hasan
{"title":"预测双耳节拍对工作记忆的影响","authors":"Ahmad Zahid Rao, Muhammad Danish Mujib, Saad Ahmed Qazi, Ahmad O Alokaily, Ayesha Ikhlaq, Eraj Humayun Mirza, Ahmed Ali Aldohbeyb, Muhammad Abul Hasan","doi":"10.1097/WNR.0000000000002101","DOIUrl":null,"url":null,"abstract":"<p><p>Working memory is vital for short-term information processing. Binaural beats can enhance working memory by improving attention and memory consolidation through neural synchronization. However, individual differences in cognitive and neuronal functioning affect effectiveness of binaural beats, necessitating personalized approaches. This study aimed to develop a machine learning model to predict binaural beats's effectiveness on working memory using electroencephalography. Sixty healthy participants underwent a 5-min electroencephalography recording, an initial working memory evaluation, 15 min of binaural beats stimulation, and a subsequent working memory evaluation using digit span tests of increasing difficulty. Recall accuracy and response times were measured. Differential scores from pre-evaluation and post-evaluation labeled participants as active or inactive to binaural beats stimulation. electroencephalography data, recorded using 14 electrodes, provided brain activity estimates across theta, alpha, beta, and gamma frequency bands, resulting in 56 features (14 channels × 4 bands) for the machine learning model. Several classifiers were tested to identify the most effective model. The weighted K-nearest neighbors model achieved the highest accuracy (90.0%) and area under the receiver operating characteristic curve (92.24%). Frontal and parietal electroencephalography channels in theta and alpha bands were crucial for classification. This study's findings offer significant clinical insights, enabling informed interventions and preventing resource inefficiency.</p>","PeriodicalId":19213,"journal":{"name":"Neuroreport","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predicting the effectiveness of binaural beats on working memory.\",\"authors\":\"Ahmad Zahid Rao, Muhammad Danish Mujib, Saad Ahmed Qazi, Ahmad O Alokaily, Ayesha Ikhlaq, Eraj Humayun Mirza, Ahmed Ali Aldohbeyb, Muhammad Abul Hasan\",\"doi\":\"10.1097/WNR.0000000000002101\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Working memory is vital for short-term information processing. Binaural beats can enhance working memory by improving attention and memory consolidation through neural synchronization. However, individual differences in cognitive and neuronal functioning affect effectiveness of binaural beats, necessitating personalized approaches. This study aimed to develop a machine learning model to predict binaural beats's effectiveness on working memory using electroencephalography. Sixty healthy participants underwent a 5-min electroencephalography recording, an initial working memory evaluation, 15 min of binaural beats stimulation, and a subsequent working memory evaluation using digit span tests of increasing difficulty. Recall accuracy and response times were measured. Differential scores from pre-evaluation and post-evaluation labeled participants as active or inactive to binaural beats stimulation. electroencephalography data, recorded using 14 electrodes, provided brain activity estimates across theta, alpha, beta, and gamma frequency bands, resulting in 56 features (14 channels × 4 bands) for the machine learning model. Several classifiers were tested to identify the most effective model. The weighted K-nearest neighbors model achieved the highest accuracy (90.0%) and area under the receiver operating characteristic curve (92.24%). Frontal and parietal electroencephalography channels in theta and alpha bands were crucial for classification. This study's findings offer significant clinical insights, enabling informed interventions and preventing resource inefficiency.</p>\",\"PeriodicalId\":19213,\"journal\":{\"name\":\"Neuroreport\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2024-12-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Neuroreport\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1097/WNR.0000000000002101\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/9/30 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q4\",\"JCRName\":\"NEUROSCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neuroreport","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1097/WNR.0000000000002101","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/9/30 0:00:00","PubModel":"Epub","JCR":"Q4","JCRName":"NEUROSCIENCES","Score":null,"Total":0}
引用次数: 0

摘要

工作记忆对短期信息处理至关重要。双耳节拍可以通过神经同步改善注意力和记忆巩固,从而增强工作记忆。然而,认知和神经元功能的个体差异会影响双耳节拍的效果,因此需要个性化的方法。本研究旨在开发一种机器学习模型,利用脑电图预测双耳节拍对工作记忆的效果。60 名健康参与者接受了 5 分钟的脑电图记录、初步工作记忆评估、15 分钟的双耳节拍刺激以及随后的工作记忆评估,评估采用的是难度递增的数字跨度测试。对回忆的准确性和反应时间进行了测量。使用 14 个电极记录的脑电图数据提供了θ、α、β和γ频段的大脑活动估计值,为机器学习模型提供了 56 个特征(14 个通道 × 4 个频段)。为了找出最有效的模型,对多个分类器进行了测试。加权 K 近邻模型获得了最高的准确率(90.0%)和接收者工作特征曲线下面积(92.24%)。额叶和顶叶脑电图通道的θ和α波段对分类至关重要。这项研究的结果为临床提供了重要的启示,使人们能够采取明智的干预措施,避免资源的低效利用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting the effectiveness of binaural beats on working memory.

Working memory is vital for short-term information processing. Binaural beats can enhance working memory by improving attention and memory consolidation through neural synchronization. However, individual differences in cognitive and neuronal functioning affect effectiveness of binaural beats, necessitating personalized approaches. This study aimed to develop a machine learning model to predict binaural beats's effectiveness on working memory using electroencephalography. Sixty healthy participants underwent a 5-min electroencephalography recording, an initial working memory evaluation, 15 min of binaural beats stimulation, and a subsequent working memory evaluation using digit span tests of increasing difficulty. Recall accuracy and response times were measured. Differential scores from pre-evaluation and post-evaluation labeled participants as active or inactive to binaural beats stimulation. electroencephalography data, recorded using 14 electrodes, provided brain activity estimates across theta, alpha, beta, and gamma frequency bands, resulting in 56 features (14 channels × 4 bands) for the machine learning model. Several classifiers were tested to identify the most effective model. The weighted K-nearest neighbors model achieved the highest accuracy (90.0%) and area under the receiver operating characteristic curve (92.24%). Frontal and parietal electroencephalography channels in theta and alpha bands were crucial for classification. This study's findings offer significant clinical insights, enabling informed interventions and preventing resource inefficiency.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Neuroreport
Neuroreport 医学-神经科学
CiteScore
3.20
自引率
0.00%
发文量
150
审稿时长
1 months
期刊介绍: NeuroReport is a channel for rapid communication of new findings in neuroscience. It is a forum for the publication of short but complete reports of important studies that require very fast publication. Papers are accepted on the basis of the novelty of their finding, on their significance for neuroscience and on a clear need for rapid publication. Preliminary communications are not suitable for the Journal. Submitted articles undergo a preliminary review by the editor. Some articles may be returned to authors without further consideration. Those being considered for publication will undergo further assessment and peer-review by the editors and those invited to do so from a reviewer pool. The core interest of the Journal is on studies that cast light on how the brain (and the whole of the nervous system) works. We aim to give authors a decision on their submission within 2-5 weeks, and all accepted articles appear in the next issue to press.
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信