优化节点级囊图形神经网络,用于从EEG信号中独立于主体的情绪识别
G Kiruthiga1, Ashwinth Janarthanan2, P D Mahendhiran3
1Department of Artificial Intelligence and data science, Karpagam College of Engineering, Coimbatore, Tamil Nadu, India.
Electromagnetic biology and medicine
|September 8, 2025
概括
这项研究引入了一种新的方法,用于使用脑电图 (EEG) 和深度学习进行主体独立的情绪检测. 增强的节点级囊图神经网络 (NCGNN) 与Piranha Foraging Optimization Algorithm (PFOA) 显著提高了情绪识别准确性和回忆.
科学领域:
- 神经科学是一个神经科学.
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 由于有限的标记数据集,从脑电图 (EEG) 中检测主体独立的情绪是具有挑战性的.
- 现有的模型往往难以在不同的人群和情绪状态中进行概括.
- 准确的情绪识别对于各种应用至关重要,包括心理健康监测和人机交互.
研究的目的:
- 开发一个强大的独立于主体的情绪检测系统,使用EEG.
- 为了提高节点级囊图神经网络 (NCGNN) 的表现,用于情感识别.
- 为了提高EEG数据的情绪分类的准确性,精度和回忆.
主要方法:
- 利用振动模式分解 (VMD) 来从EEG信号中提取特征.
- 开发了一个节点级囊图神经网络 (NCGNN) 用于情绪分类.
- 集成Piranha食优化算法 (PFOA) 来优化NCGNN参数以提高性能.
- 在Python中实现了拟议的NLCGNN-SIER-EEG模型,并使用准确度,精度,回忆,F1分数和ROC等指标评估了其性能.
主要成果:
- 拟议的NLCGNN-SIER-EEG技术在现有方法上显示出了显著的改进.
- 与SIER-EEG-VMD-DL,ERS-TLE-DCNN和EEH-HER-ANN相比,实现了更高的准确性,精度和回忆.
- 具体来说,准确度提高了19.57%,24.37%和34.15%,精度提高了22.12%,26.82%和28.52%,回忆分别提高了23.26%,28.17%和29.43%.
结论:
- 皮拉纳食优化算法有效地增强了节点级囊图形神经网络,用于从EEG中独立于主体的情绪检测.
- 拟议的NLCGNN-SIER-EEG模型为现实世界情绪识别应用提供了更准确和更强大的解决方案.
- 这种方法解决了基于EEG的情绪检测中小或偏见的数据集的局限性.
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