Automated Detection of Electroencephalography Artifacts in Human, Rodent and Canine Subjects using Machine Learning

Publication date: Available online 23 June 2018
Source:Journal of Neuroscience Methods
Author(s): Joshua Levitt, Adam Nitenson, Suguru Koyama, Lonne Heijmans, James Curry, Jason T. Ross, Steven Kamerling, Carl Y Saab
BackgroundElectroencephalography (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.New MethodWe 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.ResultsThe 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.Comparison with Existing Methods: 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.ConclusionsWe 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.



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