A Novel Approach to Cognitive Load Measurement in N-Back Tasks Using Wearable Sensors, Empirical Mode Decomposition With Machine Learning, and Explainable AI for Feature Importance

A Novel Approach to Cognitive Load Measurement in N-Back Tasks Using Wearable Sensors, Empirical Mode Decomposition With Machine Learning, and Explainable AI for Feature Importance

This study introduces a novel approach for detecting mental workload and stress, utilizing ECG and Fingertip-PPG data from the MAUS dataset. The dataset includes physiological recordings such as ECG, Fingertip-PPG, Wrist-PPG, and GSR signals from 22 participants exposed to varying levels of mental w...

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Bibliographic Details
Main Authors: Aakanksha Dayal, Ziaullah Khan, Kiramat Ullah, Hee-Cheol Kim
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Physiological signals
electrocardiogram (ECG) signal
photoplethysmography (PPG) signal
machine learning
empirical mode decomposition
mental workload
Online Access:https://ieeexplore.ieee.org/document/10817542/
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