基于EEG的跨主体情绪识别进化集体学习
IEEE journal of biomedical and health informatics
|July 2, 2024
概括
这项研究引入了一种使用进化编程和神经网络的新框架,用于更准确地从电脑电图 (EEG) 数据中跨主体识别情绪. 该方法显著提高了公共数据集的性能,帮助生物医学研究.
科学领域:
- 神经科学和生物医学工程
- 医疗保健中的人工智能
背景情况:
- 电脑电图 (EEG) 由于其时间分辨率,对情绪识别有价值.
- 跨主体情绪识别面临来自个体EEG差异和情绪复杂性的挑战.
研究的目的:
- 提出一个端到端的框架,以提高跨主体情绪识别性能.
- 为了解决基于EEG的情绪识别模型中的概括问题.
主要方法:
- 开发了一种具有进化编程 (EPNNE) 的新型神经网络组合以进行优化.
- 利用一个端到端的框架来改进跨主体的情绪识别.
- 在DEAP,FACED,SEED和SEED-IV数据集上评估了该方法.
主要成果:
- 与最先进的技术相比,提出的EPNNE方法显示出更高的性能.
- 在跨主体情绪识别准确度方面取得了显著的改进.
结论:
- 开发的端到端框架有效地改善了跨主题的情绪识别.
- 这种方法有助于生物医学研究人员评估情绪状态进行干预.
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