Decoding Functional Brain Data for Emotion Recognition: A Machine Learning Approach

IF 1.9 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Emine Elif Tülay, Tuğçe Ballı
{"title":"Decoding Functional Brain Data for Emotion Recognition: A Machine Learning Approach","authors":"Emine Elif Tülay, Tuğçe Ballı","doi":"10.1145/3657638","DOIUrl":null,"url":null,"abstract":"<p>The identification of emotions is an open research area and has a potential leading role in the improvement of socio-emotional skills such as empathy, sensitivity, and emotion recognition in humans. The current study aimed to use Event Related Potential (ERP) components (N100, N200, P200, P300, early Late Positive Potential (LPP), middle LPP, and late LPP) of EEG data for the classification of emotional states (positive, negative, neutral). EEG data were collected from 62 healthy individuals over 18 electrodes. An emotional paradigm with pictures from the International Affective Picture System (IAPS) was used to record the EEG data. A linear Support Vector Machine (C=0.1) was used to classify emotions, and a forward feature selection approach was used to eliminate irrelevant features. The early LPP component, which was the most discriminative among all ERP components, had the highest classification accuracy (70.16%) for identifying negative and neutral stimuli. The classification of negative versus neutral stimuli had the best accuracy (79.84%) when all ERP components were used as a combined feature set, followed by positive versus negative stimuli (75.00%) and positive versus neutral stimuli (68.55%). Overall, the combined ERP component feature sets outperformed single ERP component feature sets for all stimulus pairings in terms of accuracy. These findings are promising for further research and development of EEG-based emotion recognition systems.</p>","PeriodicalId":50921,"journal":{"name":"ACM Transactions on Applied Perception","volume":"221 1","pages":""},"PeriodicalIF":1.9000,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on Applied Perception","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1145/3657638","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
引用次数: 0

Abstract

The identification of emotions is an open research area and has a potential leading role in the improvement of socio-emotional skills such as empathy, sensitivity, and emotion recognition in humans. The current study aimed to use Event Related Potential (ERP) components (N100, N200, P200, P300, early Late Positive Potential (LPP), middle LPP, and late LPP) of EEG data for the classification of emotional states (positive, negative, neutral). EEG data were collected from 62 healthy individuals over 18 electrodes. An emotional paradigm with pictures from the International Affective Picture System (IAPS) was used to record the EEG data. A linear Support Vector Machine (C=0.1) was used to classify emotions, and a forward feature selection approach was used to eliminate irrelevant features. The early LPP component, which was the most discriminative among all ERP components, had the highest classification accuracy (70.16%) for identifying negative and neutral stimuli. The classification of negative versus neutral stimuli had the best accuracy (79.84%) when all ERP components were used as a combined feature set, followed by positive versus negative stimuli (75.00%) and positive versus neutral stimuli (68.55%). Overall, the combined ERP component feature sets outperformed single ERP component feature sets for all stimulus pairings in terms of accuracy. These findings are promising for further research and development of EEG-based emotion recognition systems.

为情绪识别解码大脑功能数据:机器学习方法
情绪识别是一个开放的研究领域,对于提高人类的共情、敏感和情绪识别等社会情感技能具有潜在的领导作用。本研究旨在利用脑电图数据中的事件相关电位(ERP)成分(N100、N200、P200、P300、早期正电位(LPP)、中期正电位(LPP)和晚期正电位(LPP))对情绪状态(积极、消极和中性)进行分类。研究人员通过 18 个电极收集了 62 名健康人的脑电图数据。在记录脑电图数据时,使用了国际情感图片系统(IAPS)中的情感范例图片。使用线性支持向量机(C=0.1)对情绪进行分类,并使用前向特征选择法消除无关特征。在所有 ERP 分量中,早期 LPP 分量的辨别能力最强,在识别负面和中性刺激方面的分类准确率最高(70.16%)。将所有 ERP 成分作为组合特征集时,负性与中性刺激的分类准确率最高(79.84%),其次是正性与负性刺激(75.00%)和正性与中性刺激(68.55%)。总体而言,在所有刺激配对中,组合式ERP成分特征集的准确性都优于单一ERP成分特征集。这些发现为进一步研究和开发基于脑电图的情绪识别系统带来了希望。
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 求助全文
来源期刊
ACM Transactions on Applied Perception
ACM Transactions on Applied Perception 工程技术-计算机:软件工程
CiteScore
3.70
自引率
0.00%
发文量
22
审稿时长
12 months
期刊介绍: ACM Transactions on Applied Perception (TAP) aims to strengthen the synergy between computer science and psychology/perception by publishing top quality papers that help to unify research in these fields. The journal publishes inter-disciplinary research of significant and lasting value in any topic area that spans both Computer Science and Perceptual Psychology. All papers must incorporate both perceptual and computer science components.
×
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