Comparing ANOVA and PowerShap Feature Selection Methods via Shapley Additive Explanations of Models of Mental Workload Built with the Theta and Alpha EEG Band Ratios

Comparing ANOVA and PowerShap Feature Selection Methods via Shapley Additive Explanations of Models of Mental Workload Built with the Theta and Alpha EEG Band Ratios

<b>Background</b>: Creating models to differentiate self-reported mental workload perceptions is challenging and requires machine learning to identify features from EEG signals. EEG band ratios quantify human activity, but limited research on mental workload assessment exists. This study...

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Bibliographic Details
Main Authors: Bujar Raufi, Luca Longo
Format: Article
Language:English
Published: MDPI AG 2024-03-01
Series:BioMedInformatics
Subjects:
model explainability
mental workload
statistical feature selection
Shapley-based feature selection
alpha and theta EEG band ratios
machine learning
Online Access:https://www.mdpi.com/2673-7426/4/1/48

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https://www.mdpi.com/2673-7426/4/1/48

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