# Cognitive Workload Detection of Air Traffic Controllers Based on mRMR and Fewer EEG Channels

## TL;DR

This study introduces a method to detect air traffic controllers' cognitive workload using fewer EEG channels and improved accuracy.

## Contribution

The novel approach uses mRMR and gamma wave features with only three EEG channels to accurately detect cognitive workload.

## Key findings

- A model using three EEG channels achieved high accuracy in detecting cognitive workload.
- The mRMR algorithm improved feature selection and model stability.
- The method enhances practicality and streamlines the detection process for real-world applications.

## Abstract

For air traffic controllers, the extent of their cognitive workload can significantly impact their cognitive function and response time, consequently influencing their operational efficiency or even resulting in safety incidents. In order to enhance the accuracy and efficiency in determining the cognitive workload of air traffic controllers, a cognitive workload detection method for air traffic controllers based on mRMR and fewer EEG channels was proposed in this study. First of all, a set of features related to gamma waves was initially proposed; subsequently, an EEG feature evaluation method based on the mRMR algorithm was employed to pinpoint the most relevant indicators for the detection of the cognitive workload. Consequently, a model for the detection of the cognitive workload of controllers was developed, and it was optimized by filtering out channel combinations that exhibited higher sensitivity to the workload using the mRMR algorithm. The results demonstrate that the enhanced model achieves the accuracy and stability required for practical applications. Notably, in this study, only three EEG channels were employed to achieve the highly precise detection of the cognitive workload of controllers. This approach markedly increases the practicality of employing EEG equipment for the detection of the cognitive workload and streamlines the detection process.