使用脑电图进行心理压力检测的新型优化混合深度学习框架
Maithili Shailesh Andhare1,2, T Vijayan1, B Karthik1
1Department of Electronics Communication Engineering, Bharath Institute of Higher Education and Research, Chennai 600073, India.
这项研究介绍了BDDNet,这是一个新的深度学习框架,用于通过脑电图 (EEG) 检测心理压力. 通过整合多个深度学习模型和优化算法来提高压力检测,BDDNet实现了高精度.
科学领域:
- 神经科学
- 计算机科学
- 人工智能
背景情况:
- 由各种压力引发的心理压力对人类行为产生重大影响,并且越来越多地使用脑电图 (EEG) 来研究.
- 基于EEG的现有深度学习 (DL) 方法面临诸如复杂结构,阶级不平衡和信号处理困难等挑战.
研究的目的:
- 提出一种新的混合深度学习框架,BDDNet,用于使用EEG增强心理压力检测.
- 改进EEG信号的光谱时间特征描绘和长期依赖性分析以检测压力.
主要方法:
- 开发了BDDNet,这是一个混合框架,结合了深度卷积神经网络 (DCNN),双向长期记忆 (BiLSTM) 和深度信念网络 (DBN).
- 使用多个EEG特征 (MEF) 进行全面的光谱和时间域分析.
- 采用改进的乌搜索算法 (ICSA) 进行高效的频道选择,并采用员工优化算法 (EOA) 进行超参数调整.
主要成果:
- 在公共DEAP数据集上,BDDNet-ICSA框架表现出卓越的表现.
- 实现了高度指标,包括97.6%的回忆,97.6%的精度,97.6%的F1分数,96.9%的选择性,96.9%的负预测值 (NPV) 和97.3%的准确性.
- 超过传统的压力检测技术.
结论:
- BDDNet提供了一种强大而有效的解决方案,
- 混合深度学习方法与优化算法相结合,显著提高了压力检测能力.
- 这一框架有助于推进非侵入性心理压力监测和管理.
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