Three simple steps to improve the interpretability of EEG-SVM studies.
In: Journal of Neurophysiology, Jg. 128 (2022-12-01), Heft 6, S. 1375-1382
Online
academicJournal
Zugriff:
Machine-learning systems that classify electroencephalography (EEG) data offer important perspectives for the diagnosis and prognosis of a wide variety of neurological and psychiatric conditions, but their clinical adoption 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 three 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. [ABSTRACT FROM AUTHOR]
Titel: |
Three simple steps to improve the interpretability of EEG-SVM studies.
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Autor/in / Beteiligte Person: | Joucla, Coralie ; Gabriel, Damien ; Ortega, Juan-Pablo ; Haffen, Emmanuel |
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Zeitschrift: | Journal of Neurophysiology, Jg. 128 (2022-12-01), Heft 6, S. 1375-1382 |
Veröffentlichung: | 2022 |
Medientyp: | academicJournal |
ISSN: | 0022-3077 (print) |
DOI: | 10.1152/jn.00221.2022 |
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