Three simple steps to improve the interpretability of EEG-SVM studies.
Fiche publication
Date publication
septembre 2022
Journal
Journal of neurophysiology
Auteurs
Membres identifiés du Cancéropôle Est :
Pr HAFFEN Emmanuel
Tous les auteurs :
Joucla C, Gabriel D, Ortega JP, Haffen E
Lien Pubmed
Résumé
Research in machine-learning classification of electroencephalography (EEG) data offers important perspectives for the diagnosis and prognosis of a wide variety of neurological and psychiatric conditions, but the clinical adoption of such systems remains low. We propose here that much of the difficulties translating EEG-machine learning research to the clinic result from consistent inaccuracies in their technical reporting, which severely impair the interpretability of their often-high claims of performance. Taking example from a major class of machine-learning algorithms used in EEG research, the support-vector machine (SVM), we highlight three important aspects of model building and evaluation (normalization, hyperparameter optimization and cross-validation) and show that, while these 3 aspects can make or break the performance of the system, they are left entirely undocumented in a shockingly vast majority of the research literature. Providing a more systematic description of these aspects of model development constitute three simple steps to improve the interpretability of EEG-SVM research and, in fine, its clinical adoption.
Mots clés
Electroencephalography, How-to, Reliability, Reproducibility, Support Vector Machines
Référence
J Neurophysiol. 2022 09 28;: