集群数据中的学习分类器:基于EEG的人类情绪识别的BCI模式识别模型
Raoufeh Kheirabadi1, Hesam Omranpour1
1Department of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran.
Computer methods in biomechanics and biomedical engineering
|September 5, 2023
概括
这项研究引入了一种使用脑电图 (EEG) 脑信号进行情绪识别的新方法. 通过将经验模式分解 (EMD) 与高级特征选择和聚类相结合,该模型在分类情绪方面实现了高准确性.
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:
- 从脑电图 (EEG) 信号中检测人类情绪是具有挑战性的,因为信号的复杂性和大数据大小.
- 有效的特征选择对于提高基于EEG的情绪分类模型的性能至关重要.
- 现有的方法可能无法完全捕捉EEG信号的动态和不稳定性.
研究的目的:
- 开发一种准确和有效的方法来识别来自EEG信号的情绪.
- 通过整合经验模式分解 (EMD) 与特征选择和聚类来提高分类性能.
- 用DEAP数据集验证拟议模型的有效性.
主要方法:
- 使用实证模式分解 (EMD) 将EEG信号分解为内在模式函数 (IMF).
- 使用RBF内核和LASSO特征选择,提取和改进了IMF的统计属性.
- 进行数据聚类,然后使用决策树,随机森林和KNN算法进行分类.
主要成果:
- 拟议的模型在DEAP数据集上实现了99.17%的分类准确性.
- 该方法与最近的深度学习方法相比,表现优越.
- 集群通过将数据细分成可管理的类子集,有效地提高了分类准确性.
结论:
- 电脑电图,特征选择和聚类的综合方法为基于EEG的情绪识别提供了一个高度准确的方法.
- 这种技术对分类集群数据非常有效,满足了脑计算机接口 (BCI) 系统日益增长的需求.
- 该模型的高精度突出显示了其在情绪检测中的现实应用潜力.
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