Window-Based Feature Engineering for Cognitive Workload Detection

TL;DR

This paper introduces a window-based feature extraction method combined with deep learning models to improve real-time classification of cognitive workload levels using the COLET dataset.

Contribution

It presents a novel window-based feature engineering approach and demonstrates the superiority of deep learning models over traditional machine learning for workload detection.

Findings

Deep learning models outperform traditional machine learning in accuracy and precision.

Window-based temporal features enhance classification performance.

Deep models show potential for real-time cognitive workload assessment.

Abstract

Cognitive workload is a topic of increasing interest across various fields such as health, psychology, and defense applications. In this research, we focus on classifying cognitive workload using the COLET dataset, employing a window-based approach for feature generation and machine/deep learning techniques for classification. We apply window-based temporal partitioning to enhance features used in existing research, followed by machine learning and deep learning models to classify different levels of cognitive workload. The results demonstrate that deep learning models, particularly tabular architectures, outperformed traditional machine learning methods in precision, F1-score, accuracy, and classification precision. This study highlights the effectiveness of window-based temporal feature extraction and the potential of deep learning techniques for real-time cognitive workload assessment in complex and dynamic tasks.