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在观看情感视频时识别情感:基于电脑图信号分析和机器学习模型

Afshin S Asl¹, Sahar Karimpour²

¹Department of Biomedical Engineering, Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran.

|September 29, 2025

概括

这项研究使用脑电图 (EEG) 信号和机器学习来识别幸福和悲伤等情绪. 合奏和决策树模型实现了高精度,展示了情感感知技术的潜力.

科学领域:

神经科学是一个神经科学.
计算机科学计算机科学
心理学心理学心理学

背景情况:

情绪显著影响人类的表现和心理健康.
技术进步的目标是创建情感感知系统.
电脑电图 (EEG) 对于分析情绪至关重要.

研究的目的:

使用EEG信号分析识别五种不同的情绪 (放松,快乐,动机,悲伤,恐惧).
评估各种机器学习模型在情感分类中的有效性.
为促进情感意识技术的发展做出贡献.

主要方法:

收集了23名年龄在24-31岁的男学生的EEG数据,同时他们观看了引起情绪的视频.
预处理的EEG信号去除噪声和人工物,提取统计和频域特征.
使用决策树,LDA,天真贝叶斯,SVM,KNN,Ensemble,物流回归和神经网络模型进行分类的情绪.

主要成果:

组合和决策树模型表现出最高的分类准确率,分别为95.38%和91.77%.
使用统计测试提取和选择特征是分类过程中的关键步骤.
该研究成功地根据EEG数据区分了五种情绪状态.

结论:

机器学习模型,特别是Ensemble和决策树,可以从EEG信号中准确地分类人类的情绪.
基于EEG的情绪识别在心理健康和人机交互方面具有很大的应用潜力.
进一步的研究可以探索更复杂的情绪和不同的参与者群体.

关键词:

分类模型的分类模型.电脑脑电图 (EEG) 是一种电脑电图.情绪情绪情绪情绪情绪情感识别情感识别机器学习是机器学习.

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