Machine Learning for removing EEG artifacts: Setting the benchmark
This work addresses the challenge of automated artifact recognition in EEG data for clinicians, but it is incremental as it applies existing methods to new data without introducing novel techniques.
The paper tackled the problem of EEG artifacts contaminating clinical interpretations by applying various machine learning algorithms to the world's largest open-source artifact recognition dataset, resulting in the establishment of a benchmark for future research.
Electroencephalograms (EEG) are often contaminated by artifacts which make interpreting them more challenging for clinicians. Hence, automated artifact recognition systems have the potential to aid the clinical workflow. In this abstract, we share the first results on applying various machine learning algorithms to the recently released world's largest open-source artifact recognition dataset. We envision that these results will serve as a benchmark for researchers who might work with this dataset in future.