从可穿戴设备的脑电图中对人工制品检测和人工制品类别识别技术的系统审查
Pasquale Arpaia1, Matteo De Luca1, Lucrezia Di Marino1
1Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, 80131 Naples, Italy.
Sensors (Basel, Switzerland)
|September 27, 2025
概括
本综述探讨了可穿戴电脑图 (EEG) 中的文物检测. 它强调了挑战和常见的方法,如波形变换和ICA,用于在现实世界中改善可穿戴EEG信号质量.
科学领域:
- 神经科学是一个神经科学.
- 生物医学工程 生物医学工程
背景情况:
- 可穿戴式脑电图 (EEG) 可以在临床环境之外进行真实世界的脑部监测.
- 然而,在可穿戴式EEG中,放松的采集设置往往会导致由于人工制造物的信号质量受到损害.
研究的目的:
- 系统地审查用于可穿戴EEG的文物检测和识别的方法.
- 分析与传统设置相比,可穿戴EEG中文物的特定挑战和特征.
主要方法:
- 在六个数据库中进行了系统的文献搜索,使用与EEG文物和可穿戴技术相关的特定关键字.
- 遵循了PRISMA指南,结果包括了58项研究.
- 分析侧重于文物检测,识别,移除技术和性能指标.
主要成果:
- 很少有研究专门讨论可穿戴EEG (干电极,移动性) 中独特的工件特征.
- 波形变换,ICA和基于ASR的管道对于眼睛,肌肉和运动文物来说是常见的.
- 深度学习显示出对实时文物管理的承诺,而辅助传感器未得到充分利用.
结论:
- 现有的管道经常结合检测和移除,阻碍性能评估.
- 需要一些方法来解决可穿戴EEG的特定文物特征.
- 该审查提供了管道和数据集的映射,以帮助未来的研究和可重复性.
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