从脑电图信号进行情绪分类,使用机器学习
Jesus Arturo Mendivil Sauceda1, Bogart Yail Marquez1, José Jaime Esqueda Elizondo2
1Tecnológico Nacional de México, Campus Tijuana. Calz del Tecnológico 12950, Tomas Aquino, Tijuana 22414, Mexico.
Brain sciences
|January 8, 2025
概括
神经网络在从原始脑电图 (EEG) 信号分类情绪方面表现出有限的成功,其准确度低于40%. 先进的特征提取对于改进基于EEG的情绪识别系统至关重要.
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
- 人工智能的人工智能
背景情况:
- 情绪深深影响人类的行为和健康结果.
- 电脑电图 (EEG) 信号分析为客观情绪识别提供了一条途径.
- 情绪识别方面的进步可以使医学和适应性技术个性化.
研究的目的:
- 用EEG数据评估ShallowFBCSPNet,Deep4Net和EEGNetv4用于使用EEG数据进行情绪分类.
- 在SEED-V数据集上评估这些神经网络的性能.
- 识别基于EEG的情绪识别方面的挑战和机遇.
主要方法:
- 使用了SEED-V数据集与16名参与者的EEG记录.
- 分类五种情绪状态:快乐,悲伤,厌恶,中立和恐惧.
- 训练并测试了三个不同的神经网络架构:ShallowFBCSPNet,Deep4Net和EEGNetv4.
主要成果:
- 浅FBCSPNet获得了最高的准确性 (39.13%),其次是Deep4Net (38.26%) 和EEGNetv4 (25.22%).
- 在模型中观察到显著的错误分类模式.
- 性能远远落后于最先进的方法 (例如,ResNet18的差异率为95.61%).
结论:
- 从原始EEG信号中概括情绪状态具有重大挑战.
- 先进的预处理和特征提取技术对于基于EEG的强大情绪识别至关重要.
- 这项研究提供了关于当前神经网络在这个领域的能力和局限性的基础见解.
关键词:
在Deep4Net上,我们可以找到深度4网.这是一个EEGEEGEEGEEGEEGEEGEEG.在EEGNetv4中,在ShallowFBCSPNet中使用.人工智能的人工智能是人工智能.深度学习是一种深度学习.情感识别 情感识别 情感识别机器学习是机器学习.神经网络的神经网络的神经网络更多相关视频
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