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相关概念视频

Labeling Emotion01:20

Labeling Emotion

107
Emotional labeling is a cognitive process that involves identifying and naming one's emotions, such as anger, fear, happiness, or sadness. It allows individuals to recognize and express their internal emotional states, a critical aspect of emotional regulation and communication. Labeling emotions requires more than mere recognition; it also involves drawing upon memory and contextual cues to understand the current situation and apply a corresponding emotional label. For instance, feeling...
107

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Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome
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基于脑电图的增强交叉数据集情绪识别,使用无监督域调整.

Md Niaz Imtiaz1, Naimul Khan1

  • 1Department of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, 350 Victoria St, Toronto, ON M5B 2K3, Canada.

Computers in biology and medicine
|November 16, 2024
PubMed
概括

这项研究引入了使用脑电图 (EEG) 数据进行情绪识别的新方法,提高了不同数据集的准确性,并降低了实际医疗保健应用的计算成本.

科学领域:

  • 神经科学是一个神经科学.
  • 人工智能的人工智能
  • 生物医学工程 生物医学工程

背景情况:

  • 使用脑电图 (EEG) 信号的情绪识别对医疗保健和脑电脑接口 (BCI) 有希望.
  • 跨域EEG情绪识别面临挑战,原因是标记数据的高成本和个体信号的变化.
  • 现有的方法与多样化的数据集作斗争,导致负面转移和有限的实际应用.

研究的目的:

  • 开发一种改进的方法,在不同数据分布的领域中对基于EEG的情绪进行分类.
  • 解决跨数据集情绪识别方面的挑战,包括受试者的人口统计数据,记录设备和刺激变化.
  • 通过减少计算负担来增强EEG情绪识别模型的实用性.

主要方法:

  • 拟议的渐进近距离指导目标数据选择 (GPTDS),以选择可靠的目标域样本进行培训.
  • 开发了预测信心意识测试时间增强 (PC-TTA) 以优化推断性能,降低计算成本.
  • 在DEAP和SEED数据集上实施和评估了用于跨领域情绪分类的方法.

主要成果:

  • 实现了67.44%的准确性 (DEAP到SEED) 和59.68%的准确性 (SEED到DEAP),分别超过了7.09%和6.07%的基线.
  • 在检测正面和负面情绪方面表现出有效性.
关键词:
大脑计算机接口 (BCI)电脑电图 (EEG) 是一个电脑电图.情绪识别 情绪识别测试时间增长 (TTA)无监督的域名适应

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  • 与传统的测试时间增长 (TTA) 方法相比,PC-TTA减少了15倍的计算时间.

    • 结论:

    • 拟议的GPTDS和PC-TTA方法显著提高了跨领域EEG情绪识别的准确性和效率.
    • 这些技术有效地减轻了实际应用中数据异质性和计算成本所带来的挑战.
    • 这项研究促进了对医疗保健的强大和成本效益高的影响敏感系统的开发.