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Cognitive workload estimation using physiological measures: a review.

Debashis Das Chakladar¹, Partha Pratim Roy¹

¹Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand India.

Cognitive Neurodynamics

|August 6, 2024

Summary

This summary is machine-generated.

This review analyzes physiological measures for estimating cognitive workload, crucial for understanding performance impacts. It details methods, datasets, and challenges in cognitive neuroscience research.

Keywords:

Cognitive workload Convolutional neural network Electroencephalography Long short-term memory

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Area of Science:

Cognitive Neuroscience
Human-Computer Interaction
Biomedical Engineering

Background:

Cognitive workload estimation is vital as performance suffers from overload/underload.
Physiological signals like EEG, fMRI, and eye activity are key indicators.
Existing reviews often focus narrowly on specific measures like EEG.

Purpose of the Study:

To provide a comprehensive survey of physiological measures for cognitive workload estimation.
To analyze various physiological signals and their effectiveness in workload assessment.
To identify open challenges and future research directions in the field.

Main Methods:

Systematic review of literature on physiological measures for workload estimation.
Analysis of machine learning and deep learning techniques applied to physiological data.
Examination of open-access datasets and experimental paradigms for cognitive tasks.

Main Results:

Detailed analysis of Electroencephalography (EEG), fMRI, fNIRS, respiration, and eye activity.
Comparison of different machine learning classifiers and multimodal fusion approaches.
Identification of common experimental paradigms and available datasets.

Conclusions:

A holistic approach integrating multiple physiological measures is essential for accurate workload estimation.
Significant challenges remain in standardization, real-world application, and data accessibility.
Future research should focus on developing robust, generalizable models and exploring novel physiological markers.