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对基于EEG的情感识别深度学习技术的审查:模型,方法和数据集
P Sreehari1, U Raghavendra1, Anjan Gudigar1
1Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.
F1000Research
|January 26, 2026
概括
深度学习 (DL) 增强了对情绪识别 (ER) 的脑电图 (EEG). 本综述概述了基于DL的EEG ER研究,分析数据集并指导未来的研究以获得更好的系统.
科学领域:
- 情感计算是一种情感计算.
- 神经科学是一个神经科学.
- 人工智能的人工智能
背景情况:
- 使用脑电图 (EEG) 的情绪识别 (ER) 对于理解大脑活动至关重要.
- 深度学习 (DL) 已经显著提高了使用EEG数据的ER准确性和效率.
研究的目的:
- 系统地审查基于EEG的情绪识别 (ER) 的深度学习 (DL) 方法.
- 分析EEG ER研究中使用的公共数据集.
- 引导未来的研究向更易于解释,更易于泛化和更高效的数据效率的EEG ER系统.
主要方法:
- 按照PRISMA指南进行系统的文献审查.
- 在五个主要数据库 (2020-2025) 中进行全面搜索.
- 包括使用EEG信号和DL架构用于ER的研究.
主要成果:
- 在资格选后,包括了233篇文章.
- 对有关刺激和情感表现的公共数据集的分析.
- 识别基于DL的EEG ER研究中的趋势和差距.
结论:
- 对于基于EEG的ER,DL显示出显著的前景.
- 需要进一步的研究来提高可解释性,可概括性和数据效率.
- 本次审查为开发可靠和可扩展的EEG驱动ER系统提供了路线图.
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