Mental Workload Detection and Assessment Through Statistical Features Extraction and Optimization Using GEL-RF Method for EEG Signals Using N-Back Dataset

Mental Workload Detection and Assessment Through Statistical Features Extraction and Optimization Using GEL-RF Method for EEG Signals Using N-Back Dataset

Mental workload (MW) assessment measures the cognitive effort required to perform tasks and is crucial in fields such as aviation, clinical medicine, and healthcare. This study addresses the critical need to optimize cognitive load and resource allocation by evaluating MW through EEG-based analysis....

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Bibliographic Details
Main Authors: Waleed Manzoor, Noman Naseer, Imran Fareed Nizami, Syed Hammad Nazeer, Husam A. Neamah
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Mental workload assessment (MWA)
statistical features
electroencephalogram (EEG)
genetic
evolutionary
linear (GEL) search algorithms
Online Access:https://ieeexplore.ieee.org/document/11114911/
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https://ieeexplore.ieee.org/document/11114911/

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