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Three simple steps to improve the interpretability of EEG-SVM studies
- Source :
- Journal of neurophysiology. 128(6)
- Publication Year :
- 2022
-
Abstract
- 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 development (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.
Details
- ISSN :
- 15221598
- Volume :
- 128
- Issue :
- 6
- Database :
- OpenAIRE
- Journal :
- Journal of neurophysiology
- Accession number :
- edsair.doi.dedup.....836f9c6dfaf30b5d5f73a332b4255986