使用生理同步,共享选择性注意力焦点的个人无监督集群
Ivo V Stuldreher1,2, Alexandre Merasli1, Nattapong Thammasan2
1TNO Human Factors, Netherlands Organisation for Applied Scientific Research (TNO), Soesterberg, Netherlands.
Frontiers in neuroergonomics
|January 18, 2024
概括
研究人员使用无监督的集群和来自EEG,EDA和心率的生理同步来通过共同的注意力集中来分组个人. 多模式方法,特别是将EEG与其他措施相结合,提高了现实世界注意力监测的准确性.
科学领域:
- 神经科学是一个神经科学.
- 认知科学 认知科学
- 信号处理 信号处理
背景情况:
- 在现实环境中监测注意力状态是具有挑战性的,因为标记数据有限.
- 传统方法需要特定的刺激知识和标记数据来训练注意力模型.
研究的目的:
- 研究无监督聚类与生理同步相结合,用于自动识别共享的注意力焦点.
- 评估电脑电图 (EEG),皮电活动 (EDA) 和心率在区分注意力组的有效性.
主要方法:
- 应用维度缩小和无监督的集群到生理同步数据 (EEG,EDA,心率).
- 分析了单模式 (EEG,EDA,心率分别) 和多模式 (组合) 数据.
- 使用了26名参与者的数据,他们收听一本带有间隔声音的有声书,有不同的注意力指令.
主要成果:
- 在EEG生理同步的无监督聚类中,在识别注意力群体方面,获得了最大85%的准确性.
- 将EEG与EDA和/或心率相结合的多模式方法通常比单独使用EEG获得更高的准确性.
- 与单模数据相比,多模数据分类在不同的算法中更一致.
结论:
- 基于生理同步的注意力群的无监督分类是可行的,即使不了解刺激或明确的标签.
- 将EEG与其他生理测量相结合,可以提高自然环境中注意力监测的准确性和稳定性.
- 这种方法支持未来研究日常环境中的注意力参与.
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