Department of Neurosurgery, Rhode Island Hospital, and Department of Neuroscience, Brown University, Providence, RI, USA; Department of Neuroscience, Brown University, Providence, RI, USA.
Department of Neuroscience, Brown University, Providence, RI, USA.
J Neurosci Methods. 2018 Sep 1;307:53-59. doi: 10.1016/j.jneumeth.2018.06.014. Epub 2018 Jun 23.
Electroencephalography (EEG) invariably contains extra-cranial artifacts that are commonly dealt with based on qualitative and subjective criteria. Failure to account for EEG artifacts compromises data interpretation.
We have developed a quantitative and automated support vector machine (SVM)-based algorithm to accurately classify artifactual EEG epochs in awake rodent, canine and humans subjects. An embodiment of this method also enables the determination of 'eyes open/closed' states in human subjects.
The levels of SVM accuracy for artifact classification in humans, Sprague Dawley rats and beagle dogs were 94.17%, 83.68%, and 85.37%, respectively, whereas 'eyes open/closed' states in humans were labeled with 88.60% accuracy. Each of these results was significantly higher than chance.
Other existing methods, like those dependent on Independent Component Analysis, have not been tested in non-human subjects, and require full EEG montages, instead of only single channels, as this method does.
We conclude that our EEG artifact detection algorithm provides a valid and practical solution to a common problem in the quantitative analysis and assessment of EEG in pre-clinical research settings across evolutionary spectra.
脑电图（EEG）总是包含颅外伪迹，这些伪迹通常基于定性和主观标准进行处理。如果不考虑 EEG 伪迹，将会影响数据解释。
我们开发了一种定量的和自动化的基于支持向量机（SVM）的算法，用于准确分类清醒的啮齿动物、犬科动物和人类受试者的伪迹 EEG 时段。该方法的一个体现也能够确定人类受试者的“睁眼/闭眼”状态。
SVM 在人类、斯普拉格-道利大鼠和比格犬中的伪迹分类准确性分别为 94.17%、83.68%和 85.37%，而人类的“睁眼/闭眼”状态的准确性为 88.60%。这些结果都显著高于随机水平。
其他现有的方法，如依赖于独立成分分析的方法，尚未在非人类受试者中进行测试，并且需要完整的 EEG 导联，而不是像该方法那样仅使用单个导联。
我们得出结论，我们的 EEG 伪迹检测算法为临床前研究中在进化范围内对 EEG 进行定量分析和评估的常见问题提供了一种有效且实用的解决方案。
