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Reproducible machine learning research in mental workload classification using EEG
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Date
2024-01-01
Author
Demirezen, Güliz
Taşkaya Temizel, Tuğba
Brouwer, Anne-Marie
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This study addresses concerns about reproducibility in scientific research, focusing on the use of electroencephalography (EEG) and machine learning to estimate mental workload. We established guidelines for reproducible machine learning research using EEG and used these to assess the current state of reproducibility in mental workload modeling. We first started by summarizing the current state of reproducibility efforts in machine learning and in EEG. Next, we performed a systematic literature review on Scopus, Web of Science, ACM Digital Library, and Pubmed databases to find studies about reproducibility in mental workload prediction using EEG. All of this previous work was used to formulate guidelines, which we structured along the widely recognized Cross-Industry Standard Process for Data Mining (CRISP-DM) framework. By using these guidelines, researchers can ensure transparency and comprehensiveness of their methodologies, therewith enhancing collaboration and knowledge-sharing within the scientific community, and enhancing the reliability, usability and significance of EEG and machine learning techniques in general. A second systematic literature review extracted machine learning studies that used EEG to estimate mental workload. We evaluated the reproducibility status of these studies using our guidelines. We highlight areas studied and overlooked and identify current challenges for reproducibility. Our main findings include limitations on reporting performance on unseen test data, open sharing of data and code, and reporting of resources essential for training and inference processes.
Subject Keywords
brain-computer interface
,
EEG
,
machine learning
,
mental workload
,
neuroergonomics
,
neuroscience
,
physiological measurement
,
reproducibility
URI
https://hdl.handle.net/11511/110368
Journal
Frontiers in Neuroergonomics
DOI
https://doi.org/10.3389/fnrgo.2024.1346794
Collections
Graduate School of Informatics, Article
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
G. Demirezen, T. Taşkaya Temizel, and A.-M. Brouwer, “Reproducible machine learning research in mental workload classification using EEG,”
Frontiers in Neuroergonomics
, vol. 5, pp. 0–0, 2024, Accessed: 00, 2024. [Online]. Available: https://hdl.handle.net/11511/110368.