开发一种情绪识别系统,使用电脑图和心电图信号之间的联合连接
Javid Farhadi Sedehi1, Nader Jafarnia Dabanloo1, Keivan Maghooli1
1Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Heliyon
|February 3, 2025
概括
这项研究通过结合脑电图 (EEG) 和心电图 (ECG) 数据来增强情绪识别. 新的有效连接方法显著提高了准确性,证明了多式联络生理信号的力量.
科学领域:
- 物理计算的物理计算.
- 情感计算是一种情感计算.
- 生物医学信号处理
背景情况:
- 情绪识别 (ER) 系统通常依赖单模数据,限制准确性.
- 了解大脑和心脏活动之间的复杂相互作用对于强大的ER非常重要.
研究的目的:
- 开发和评估一种创新的方法,通过整合脑电图 (EEG) 和心电图 (ECG) 数据来提高ER的准确性.
- 提出一种用于估计有效连接 (EC) 的新方法,以捕捉情绪状态期间的心脑动态.
主要方法:
- 使用了三个EC估计技术:格兰杰因果关系 (GC),部分定向连贯性 (PDC) 和定向转移函数 (DTF).
- 使用卷积神经网络 (CNN),特别是ResNet-18和MobileNetV2,来处理EC表示.
- 评估了使用公开MAHNOB-HCI数据库中的EEG和ECG数据的方法,并进行了5倍交叉验证.
主要成果:
- 在使用ResNet-18.8.使用alpha频段的DTF图像中达到97.34%±1.19%的高平均精度.
- 移动NetV2也表现出强的性能,使用DTF图像的准确率为96.53%±3.54%.
- 对比分析证实了与现有ER方法相比的大幅改善.
结论:
- 通过新的EC估计来整合EEG和ECG数据,显著提高了情绪识别性能.
- 拟议的多式联运方式为ER系统提供了更准确,更可靠的解决方案.
- 这项研究强调了利用心脑相互作用来实现先进的情感计算的有效性.
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