Artifact detection in EEG using machine learning

The electroencephalography (EEG) data records vast amounts of human cerebral activity yet is still reviewed primarily by human readers. Most of the times, the data is contaminated with non-cerebral originated signals, called artifacts, which could be very difficult to visually detect and, undiscovered, could damage the neural information analysis. The purpose of our work is to detect the artifacts by identifying the most relevant features, both in the temporal and frequency domains, and train various supervised learning algorithms: Decision Trees, SVM and KNN, in order to distinguish between clean and contaminated signals. The performance of our method exceeds the ones achieved in literature with an accuracy of detection of 98.78%, 98.30% for precision and 98.40% for recall, for the best settings we found.