Hybrid statistical and machine learning modeling of cognitive neuroscience data

Détails bibliographiques
Publié dans:	Journal of applied statistics. - 1991. - 51(2024), 6 vom: 01., Seite 1076-1097
Auteur principal:	Cakar, Serenay (Auteur)
Autres auteurs:	Yavuz, Fulya Gokalp
Format:	Article en ligne
Langue:	English
Publié:	2024
Accès à la collection:	Journal of applied statistics
Sujets:	Journal Article Machine learning cognitive studies fNIRS mixed model n-back data

Description
Résumé:	© 2023 Informa UK Limited, trading as Taylor & Francis Group. The nested data structure is prevalent for cognitive measure experiments due to repeatedly taken observations from different brain locations within subjects. The analysis methods used for this data type should consider the dependency structure among the repeated measurements. However, the dependency assumption is mainly ignored in the cognitive neuroscience data analysis literature. We consider both statistical, and machine learning methods extended to repeated data analysis and compare distinct algorithms in terms of their advantage and disadvantages. Unlike basic algorithm comparison studies, this article analyzes novel neuroscience data considering the dependency structure for the first time with several statistical and machine learning methods and their hybrid forms. In addition, the fitting performances of different algorithms are compared using contaminated data sets, and the cross-validation approach. One of our findings suggests that the GLMM tree, including random term indices indicating the location of functional near-infrared spectroscopy optodes nested within experimental units, shows the best predictive performance with the lowest MSE, RMSE, and MAE model performance metrics. However, there is a trade-off between accuracy and speed since this algorithm is required the highest computational time
Description:	Date Revised 25.04.2024 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE
ISSN:	0266-4763
DOI:	10.1080/02664763.2023.2176834