基于BMNABC的脑电多尺度熵数据内隐学习分类特征选择改进评价

Chayapol Chaiyanan, B. Kaewkamnerdpong
{"title":"基于BMNABC的脑电多尺度熵数据内隐学习分类特征选择改进评价","authors":"Chayapol Chaiyanan, B. Kaewkamnerdpong","doi":"10.1109/BMEiCON56653.2022.10012069","DOIUrl":null,"url":null,"abstract":"Those who are good at implicit learning can learn things faster and are more adaptable in the fast pace age of information. Implicit learning is a type of learning without being explicitly taught. It’s commonly seen in younger children when they develop their ability to speak their native language without learning grammar. The human brain can be trained to be good at learning by training the brain to be in the state of learning more often. By using neurofeedback to regulate the human brain state, educators and learners can help each other in training the brain to be better implicit learners. Our research aims to classify implicit learning events from EEG signals to help identify and moderate such states. This paper analyzed the feature selection process section to improve classification performance. We used previously measured participants' EEG signals while performing cognitive task experiments. Those signals were then getting feature extracted into Multiscale Entropy. Previously, Artificial Bee Colony (ABC) was used on the Multiscale Entropy to help classify the implicit learning events with reasonable success. However, an improvement was required to make the entire system more optimized due to how features being selected were in a binary search space. Binary Multi-Neighborhood Artificial Bee Colony (BMNABC) was chosen as an alternative. The comparison indicated that BMNABC increased the accuracy to as high as 90.57% and can be regarded as a promising method for identifying implicit learning events.","PeriodicalId":177401,"journal":{"name":"2022 14th Biomedical Engineering International Conference (BMEiCON)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Evaluating Improvement on Feature Selection for Classification of Implicit Learning on EEG’s Multiscale Entropy Data using BMNABC\",\"authors\":\"Chayapol Chaiyanan, B. Kaewkamnerdpong\",\"doi\":\"10.1109/BMEiCON56653.2022.10012069\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Those who are good at implicit learning can learn things faster and are more adaptable in the fast pace age of information. Implicit learning is a type of learning without being explicitly taught. It’s commonly seen in younger children when they develop their ability to speak their native language without learning grammar. The human brain can be trained to be good at learning by training the brain to be in the state of learning more often. By using neurofeedback to regulate the human brain state, educators and learners can help each other in training the brain to be better implicit learners. Our research aims to classify implicit learning events from EEG signals to help identify and moderate such states. This paper analyzed the feature selection process section to improve classification performance. We used previously measured participants' EEG signals while performing cognitive task experiments. Those signals were then getting feature extracted into Multiscale Entropy. Previously, Artificial Bee Colony (ABC) was used on the Multiscale Entropy to help classify the implicit learning events with reasonable success. However, an improvement was required to make the entire system more optimized due to how features being selected were in a binary search space. Binary Multi-Neighborhood Artificial Bee Colony (BMNABC) was chosen as an alternative. The comparison indicated that BMNABC increased the accuracy to as high as 90.57% and can be regarded as a promising method for identifying implicit learning events.\",\"PeriodicalId\":177401,\"journal\":{\"name\":\"2022 14th Biomedical Engineering International Conference (BMEiCON)\",\"volume\":\"5 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 14th Biomedical Engineering International Conference (BMEiCON)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BMEiCON56653.2022.10012069\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 14th Biomedical Engineering International Conference (BMEiCON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BMEiCON56653.2022.10012069","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

那些擅长内隐学习的人可以更快地学习东西,在快节奏的信息时代更能适应。内隐学习是一种没有明确教导的学习。这种情况常见于年龄较小的孩子,他们在没有学习语法的情况下发展说母语的能力。人类的大脑可以通过训练大脑更经常地处于学习的状态来训练出善于学习的能力。通过使用神经反馈来调节人类的大脑状态,教育者和学习者可以互相帮助,训练大脑成为更好的内隐学习者。我们的研究旨在对脑电信号中的内隐学习事件进行分类,以帮助识别和调节这种状态。本文分析了特征选择过程部分,以提高分类性能。我们在进行认知任务实验时使用了先前测量的参与者的脑电图信号。然后将这些信号的特征提取到多尺度熵中。以前,人工蜂群(Artificial Bee Colony, ABC)在多尺度熵上被用来帮助内隐学习事件分类,并取得了一定的成功。然而,由于在二进制搜索空间中选择特征的方式,需要进行改进以使整个系统更加优化。选择二元多邻域人工蜂群(BMNABC)作为替代方案。结果表明,BMNABC的准确率高达90.57%,是一种很有前途的内隐学习事件识别方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluating Improvement on Feature Selection for Classification of Implicit Learning on EEG’s Multiscale Entropy Data using BMNABC
Those who are good at implicit learning can learn things faster and are more adaptable in the fast pace age of information. Implicit learning is a type of learning without being explicitly taught. It’s commonly seen in younger children when they develop their ability to speak their native language without learning grammar. The human brain can be trained to be good at learning by training the brain to be in the state of learning more often. By using neurofeedback to regulate the human brain state, educators and learners can help each other in training the brain to be better implicit learners. Our research aims to classify implicit learning events from EEG signals to help identify and moderate such states. This paper analyzed the feature selection process section to improve classification performance. We used previously measured participants' EEG signals while performing cognitive task experiments. Those signals were then getting feature extracted into Multiscale Entropy. Previously, Artificial Bee Colony (ABC) was used on the Multiscale Entropy to help classify the implicit learning events with reasonable success. However, an improvement was required to make the entire system more optimized due to how features being selected were in a binary search space. Binary Multi-Neighborhood Artificial Bee Colony (BMNABC) was chosen as an alternative. The comparison indicated that BMNABC increased the accuracy to as high as 90.57% and can be regarded as a promising method for identifying implicit learning events.
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
自引率
0.00%
发文量
0
×
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