人类情绪状态的分类基于使用电脑电图的价值激发量表
Gs Shashi Kumar1, Niranjana Sampathila2, Roshan Joy Martis3
1Department of Electronics and Communication, Manipal Institute of Technology, Manipal Academy of Higher Education (MAHE), Manipal, Karnataka, India.
Journal of medical signals and sensors
|July 14, 2023
概括
这项研究开发了一种使用脑电图 (EEG) 信号来检测快乐和悲伤状态的情绪识别系统. 额头EEG特征实现了93.25%的准确性,简化了用于临床使用的情绪检测.
科学领域:
- 神经科学是一个神经科学.
- 情感计算是一种情感计算.
- 生物医学工程 生物医学工程
背景情况:
- 人类情感识别对于情感计算至关重要.
- 电脑电图 (EEG) 信号提供了一种非侵入性的方法来研究与情绪相关的大脑活动.
- 使用EEG对快乐和悲伤等情绪进行分类,这给研究带来了重大挑战.
研究的目的:
- 提出一种情感识别框架,使用一个二维的价值激发模型.
- 为了分类高兴奋-积极价值 (快乐) 和低兴奋-消极价值 (悲伤) 的情绪.
- 用人工神经网络 (ANN) 来评估时间,频率,统计和非线性领域的34个特征的有效性.
主要方法:
- 从脑电图信号中提取了34个截然不同的特征,这些特征遍及头皮的前部,顶部,部和部区域.
- 利用人工神经网络 (ANN) 来分类情绪状态.
- 将ANN模型的性能与不同电极位置的特征进行训练.
主要成果:
- 用额头头皮区域的特征进行训练的ANN模型获得了最高的准确率93.25%.
- 从额头区域提取的特征显示出与其他区域相比,性能优越.
- 该研究发现,一组较小的电极可以简化EEG数据的获取和处理以识别情绪.
结论:
- 额头EEG信号对于准确的情绪识别是非常有效的.
- 开发的框架简化了基于EEG的情绪检测,有助于临床应用.
- 这项研究支持可用于实时情绪监测和分析的可穿戴传感器的开发.
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