EEG-based affective brain-computer interfaces: recent advancements and future challenges.

IF 3.8
Yuxin Chen, Yong Peng, Jiajia Tang, Tracey Camilleri, Kenneth Camilleri, Wanzeng Kong, Andrzej Cichocki
{"title":"EEG-based affective brain-computer interfaces: recent advancements and future challenges.","authors":"Yuxin Chen, Yong Peng, Jiajia Tang, Tracey Camilleri, Kenneth Camilleri, Wanzeng Kong, Andrzej Cichocki","doi":"10.1088/1741-2552/ade290","DOIUrl":null,"url":null,"abstract":"<p><p><i>Objective</i>. As one of the most popular brain-computer interface (BCI) paradigms, affective BCI (aBCI) decodes the human emotional states from brain signals and imposes necessary feedback to achieve neural regulation when negative emotional states (i.e. depression, anxiety) are detected, which are considered as the two basic functions of aBCI systems. Electroencephalogram (EEG) is the scalp reflection of neural activities and has been regarded as the gold standard of emotional effects. Recently, rapid progresses have been made for emotion recognition and regulation with the purpose of constructing a high-performance closed-loop EEG-based aBCI system. Therefore, it is necessary to make a timely review for aBCI research by summarizing the current progresses as well as challenges and opportunities, to draw the attention from both academia and industry. Toward this goal, a systematic literature review was performed to summarize not only the recent progresses in emotion recognition and regulation from the perspective of closed-loop aBCI, but also the main challenges and future research focuses to narrow the gap between the current research and real applications of aBCI systems.<i>Approach</i>. A systematic literature review on EEG-based emotion recognition and regulation was performed on Web of Science and related databases, resulting in more than 100 identified studies. These studies were analyzed according to the experimental paradigm, emotion recognition methods in terms of different scenarios, and the applications of emotion recognition in diagnosis and regulation of affective disorders.<i>Main results</i>. Based on the literature review, advancements for EEG-based aBCI research were extensively summarized from six aspects including the 'emotion elicitation paradigms and data sets', 'inner exploration of EEG information', 'outer extension of fusing EEG with other data modalities', 'cross-scene emotion recognition', 'emotion recognition by considering real scenarios', and 'diagnosis and regulation of affective disorders'. In addition, future opportunities were concluded by focusing on the main challenges in hindering the aBCI system to move from laboratory to real applications. Moreover, the neural mechanisms and theoretical basis behind EEG emotion recognition and regulation are also introduced to provide support for the advancements and challenges in aBCI.<i>Significance</i>. This review summarizes the current practices and performance outcomes in emotion recognition and regulation. Future directions in response to the existing challenges are provided with the expectation of guiding the aBCI research to focus on the necessary key technologies of aBCI systems in practical deployment.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8000,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of neural engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1088/1741-2552/ade290","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

Objective. As one of the most popular brain-computer interface (BCI) paradigms, affective BCI (aBCI) decodes the human emotional states from brain signals and imposes necessary feedback to achieve neural regulation when negative emotional states (i.e. depression, anxiety) are detected, which are considered as the two basic functions of aBCI systems. Electroencephalogram (EEG) is the scalp reflection of neural activities and has been regarded as the gold standard of emotional effects. Recently, rapid progresses have been made for emotion recognition and regulation with the purpose of constructing a high-performance closed-loop EEG-based aBCI system. Therefore, it is necessary to make a timely review for aBCI research by summarizing the current progresses as well as challenges and opportunities, to draw the attention from both academia and industry. Toward this goal, a systematic literature review was performed to summarize not only the recent progresses in emotion recognition and regulation from the perspective of closed-loop aBCI, but also the main challenges and future research focuses to narrow the gap between the current research and real applications of aBCI systems.Approach. A systematic literature review on EEG-based emotion recognition and regulation was performed on Web of Science and related databases, resulting in more than 100 identified studies. These studies were analyzed according to the experimental paradigm, emotion recognition methods in terms of different scenarios, and the applications of emotion recognition in diagnosis and regulation of affective disorders.Main results. Based on the literature review, advancements for EEG-based aBCI research were extensively summarized from six aspects including the 'emotion elicitation paradigms and data sets', 'inner exploration of EEG information', 'outer extension of fusing EEG with other data modalities', 'cross-scene emotion recognition', 'emotion recognition by considering real scenarios', and 'diagnosis and regulation of affective disorders'. In addition, future opportunities were concluded by focusing on the main challenges in hindering the aBCI system to move from laboratory to real applications. Moreover, the neural mechanisms and theoretical basis behind EEG emotion recognition and regulation are also introduced to provide support for the advancements and challenges in aBCI.Significance. This review summarizes the current practices and performance outcomes in emotion recognition and regulation. Future directions in response to the existing challenges are provided with the expectation of guiding the aBCI research to focus on the necessary key technologies of aBCI systems in practical deployment.

基于脑电图的情感脑机接口:最新进展和未来挑战。
目的:情感脑机接口(affective BCI, aBCI)是目前最流行的脑机接口(brain-computer interface, BCI)范式之一,它将人的情绪状态从大脑信号中解码出来,并在检测到消极情绪状态(如抑郁、焦虑)时施加必要的反馈以实现神经调节,被认为是aBCI系统的两大基本功能。脑电图(EEG)是头皮神经活动的反映,被认为是情绪影响的金标准。近年来,以构建高性能闭环脑电图为基础的aBCI系统为目标的情绪识别与调控研究取得了快速进展。因此,有必要对aBCI研究进行及时的回顾,总结当前的进展以及面临的挑战和机遇,以引起学术界和业界的重视。为此,本文通过系统的文献综述,总结了闭环aBCI视角下情绪识别与调控的最新进展,以及当前面临的主要挑战和未来的研究重点,以缩小当前研究与aBCI系统实际应用之间的差距。在Web of Science及相关数据库上对基于脑电图的情绪识别与调节进行了系统的文献综述,共收录了100余篇相关研究。从实验范式、不同情境下的情绪识别方法、情绪识别在情感性障碍诊断与调节中的应用等方面对这些研究进行了分析。& # xD;主要结果。在文献综述的基础上,从“情绪激发范式与数据集”、“脑电信息的内部探索”、“脑电与其他数据模式融合的外部扩展”、“跨场景情绪识别”、“基于真实场景的情绪识别”、“情感障碍的诊断与调控”六个方面对基于脑电的aBCI研究进展进行了综述。此外,通过关注阻碍aBCI系统从实验室转向实际应用的主要挑战,总结了未来的机会。此外,本文还介绍了EEG情绪识别与调节的神经机制和理论基础,为aBCI研究的进展和挑战提供支持。 ;本文综述了情绪识别和调节的研究现状和结果。针对存在的挑战,提出了未来的发展方向,期望指导aBCI研究在实际部署中关注aBCI系统所需的关键技术。
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
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:604180095
Book学术官方微信