合作色龙搜索优化启用了使用EEG信号识别情绪的深层组合分类器
1Department of Computer Science, College of Computing and Informatics, Saudi Electronic University, Saudi Arabia.
Computer methods in biomechanics and biomedical engineering
|October 13, 2025
概括
这项研究引入了一种新的深度学习模型,用于使用脑电图 (EEG) 信号识别情绪. 协作驼搜索优化 (CC-DERN) 模型在检测情绪方面取得了很高的准确性.
科学领域:
- 神经科学是一个神经科学.
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 准确的情绪识别对于理解行为和认知功能至关重要.
- 现有的脑电图 (EEG) 信号分析方法在特征提取和对象依赖方面面临挑战.
- 开发高效的情绪识别技术对于各种应用是必不可少的.
研究的目的:
- 提出一种使用EEG信号的有效情绪识别模型.
- 解决当前方法中特征提取和对象依赖性的局限性.
- 从EEG数据中提高情绪检测的准确性和可靠性.
主要方法:
- 开发了一种支持合作驼搜索优化的深层集体卷积循环神经网络 (CC-DERN) 分类器.
- 使用统计特征,频域特征 (FDF),时间域特征 (TDF) 和VGG-16进行特征提取.
- 实施了增强的预处理技术,以删除EEG信号中的工件.
- 雇员合作驼搜索优化用于歧视性特征选择.
主要成果:
- 在DEAP和SEED数据集上,CC-DERN模型实现了高性能指标.
- DEAP数据集的结果:准确率为96.09%,灵敏度为99.551%,特异性为93.43%.
- SEED数据集的结果:98%的准确性,99.551%的敏感性和84.024%的特异性.
- 与传统情绪识别技术相比,表现出更高的性能.
结论:
- CC-DERN模型提供了一个强大而准确的解决方案,用于从EEG信号识别情绪.
- 整合合作驼搜索优化显著提高了特征选择和检测准确度.
- 拟议的方法有效地克服了在基于EEG的情绪识别中与人工物移除和对象依赖性相关的挑战.
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