IEEE Trans Neural Syst Rehabil Eng. 2019 Feb;27(2):244-255. doi: 10.1109/TNSRE.2019.2893113. Epub 2019 Jan 15.
Electroencephalographic (EEG) recordings are contaminated by instrumental, environmental, and biological artifacts, resulting in low signal-to-noise ratio. Artifact detection is a critical task for real-time applications where the signal is used to give a continuous feedback to the user. In these applications, it is therefore necessary to estimate online a signal quality index (SQI) in order to stop the feedback when the signal quality is unacceptable. In this paper, we introduce the Riemannian potato field (RPF) algorithm as such SQI. It is a generalization and extensionof theRiemannian potato, a previouslypublished real-time artifact detection algorithm, whose performance is degraded as the number of channels increases. The RPF overcomes this limitation by combining the outputs of several smaller potatoes into a unique SQI resulting in a higher sensitivity and specificity, regardless of the number of electrodes. We demonstrate these results on a clinical dataset totalizing more than 2200 h of EEG recorded at home, that is, in a non-controlled environment.
脑电图（EEG）记录受到仪器、环境和生物伪影的污染，导致信噪比低。伪影检测是实时应用的关键任务，在这些应用中，信号被用来给用户提供连续的反馈。因此，在这些应用中，有必要在线估计信号质量指数（SQI），以便在信号质量不可接受时停止反馈。在本文中，我们介绍了黎曼土豆场（RPF）算法作为这种 SQI。它是以前发表的实时伪影检测算法——黎曼土豆的推广和扩展，随着通道数量的增加，其性能会降低。RPF 通过将几个较小的土豆的输出组合成一个独特的 SQI 来克服这一限制，从而提高了灵敏度和特异性，而与电极数量无关。我们在一个临床数据集上证明了这些结果，该数据集总计超过 2200 小时在家中记录的 EEG，即在非控制环境中记录的 EEG。
