AUTHOR=Rosenfelder Martin Justinus , Spiliopoulou Myra , Hoppenstedt Burkhard , Pryss Rüdiger , Fissler Patrick , della Piedra Walter Mario , Kolassa Iris-Tatjana , Bender Andreas 

TITLE=Stability of mental motor-imagery classification in EEG depends on the choice of classifier model and experiment design, but not on signal preprocessing

JOURNAL=Frontiers in Computational Neuroscience

VOLUME=17

YEAR=2023

URL=https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2023.1142948

DOI=10.3389/fncom.2023.1142948

ISSN=1662-5188

ABSTRACT=<sec><title>Introduction</title><p>Modern consciousness research has developed diagnostic tests to improve the diagnostic accuracy of different states of consciousness via electroencephalography (EEG)-based mental motor imagery (MI), which is still challenging and lacks a consensus on how to best analyse MI EEG-data. An optimally designed and analyzed paradigm must detect command-following in all healthy individuals, before it can be applied in patients, e.g., for the diagnosis of disorders of consciousness (DOC).</p></sec><sec><title>Methods</title><p>We investigated the effects of two important steps in the raw signal preprocessing on predicting participant performance (F1) and machine-learning classifier performance (area-under-curve, AUC) in eight healthy individuals, that are based solely on MI using high-density EEG (HD-EEG): artifact correction (manual correction with vs. without Independent Component Analysis [ICA]), region of interest (ROI; motor area vs. whole brain), and machine-learning algorithm (support-vector machine [SVM] vs. k-nearest neighbor [KNN]).</p></sec><sec><title>Results</title><p>Results revealed no significant effects of artifact correction and ROI on predicting participant performance (F1) and classifier performance (AUC) scores (all <italic>p</italic>s &gt; 0.05) in the SVM classification model. In the KNN model, ROI had a significant influence on the classifier performance [<italic>F</italic><sub>(1,8.939)</sub> = 7.585, <italic>p</italic> = 0.023]. There was no evidence for artifact correction and ROI selection changing the prediction of participants performance and classifier performance in EEG-based mental MI if using SVM-based classification (71–100% correct classifications across different signal preprocessing methods). The variance in the prediction of participant performance was significantly higher when the experiment started with a resting-state compared to a mental MI task block [<italic>X</italic><sup>2</sup><sub>(1)</sub> = 5.849, <italic>p</italic> = 0.016].</p></sec><sec><title>Discussion</title><p>Overall, we could show that classification is stable across different modes of EEG signal preprocessing when using SVM models. Exploratory analysis gave a hint toward potential effects of the sequence of task execution on the prediction of participant performance, which should be taken into account in future studies.</p></sec>