The necessity of leave one subject out (LOSO) cross validation for EEG disease diagnosis

摘要: High variability between individual subjects and recording sessions is a known fact about scalp recorded EEG signal. While some do, the majority of the EEG based machine learning studies do not attempt to assess performance of algorithms across recording sessions or across subjects, instead studies use the whole data-set available for training and testing, using an established k-fold cross validation technique and thus missing performance in a real-life setting on an unseen subject. This study primarily aimed to show how important is to have a leave-one-subject-out (LOSO) evaluation done for any scalp recorded EEG based machine learning. This study also demonstrates effectiveness of a Multilayer Perceptron (MLP) in getting good LOSO accuracy from balanced, clean EEG data, without any pre-processing in comparison with traditional machine learning algorithms. The study used data from …