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

Frontiers in Computational Neuroscience

Neuroscience

Burkhard Hoppenstedt¹  Rüdiger Pryss²  Iris-Tatjana Kolassa³  Patrick Fissler⁴  Martin Justinus Rosenfelder⁵  Myra Spiliopoulou⁶  Andreas Bender⁷  Mario della Piedra Walter⁸ 

[1] Institute of Databases and Information Systems, Ulm University, Ulm, Germany;Institute of Databases and Information Systems, Ulm University, Ulm, Germany;Institute of Clinical Epidemiology and Biometry, University of Würzburg, Würzburg, Germany;Institute of Psychology and Education, Clinical and Biological Psychology, Ulm University, Ulm, Germany;Institute of Psychology and Education, Clinical and Biological Psychology, Ulm University, Ulm, Germany;Psychiatric Services Thurgau, Münsterlingen, Switzerland;University Hospital for Psychiatry and Psychotherapy, Paracelsus Medical University, Salzburg, Austria;Institute of Psychology and Education, Clinical and Biological Psychology, Ulm University, Ulm, Germany;Therapiezentrum Burgau, Burgau, Germany;Knowledge Management and Discovery Lab, Faculty of Computer Science, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany;Therapiezentrum Burgau, Burgau, Germany;Department of Neurology, University of Munich, Munich, Germany;Therapiezentrum Burgau, Burgau, Germany;Faculty 2: Biology/Chemistry, University of Bremen, Bremen, Germany;

关键词: motor-imagery;    encephalography;    machine learning;    support vector machine;    k-nearest neighbors;    classification;    classifier;    accuracy;   

DOI  :  10.3389/fncom.2023.1142948

 received in 2023-01-12, accepted in 2023-04-05,  发布年份 2023

来源: Frontiers

【 摘 要 】

IntroductionModern 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).MethodsWe 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]).ResultsResults revealed no significant effects of artifact correction and ROI on predicting participant performance (F1) and classifier performance (AUC) scores (all ps > 0.05) in the SVM classification model. In the KNN model, ROI had a significant influence on the classifier performance [F(1,8.939) = 7.585, p = 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 [X2(1) = 5.849, p = 0.016].DiscussionOverall, 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.

【 授权许可】

Unknown   
Copyright © 2023 Rosenfelder, Spiliopoulou, Hoppenstedt, Pryss, Fissler, della Piedra Walter, Kolassa and Bender.