Cognitive Workload Recognition Using EEG Signals and Machine Learning: A Review

Machine learning and its subfield deep learning techniques provide opportunities for the development of operator mental state monitoring, especially for cognitive workload recognition using electroencephalogram (EEG) signals. Although a variety of machine learning methods have been proposed for recognizing cognitive workload via EEG recently, there does not yet exist a review that covers in-depth the application of machine learning methods. To alleviate this gap, in this article, we survey cognitive workload and machine learning literature to identify the approaches and highlight the primary advances. To be specific, we first introduce the concepts of cognitive workload and machine learning. Then, we discuss the steps of classical machine learning for cognitive workload recognition from the following aspects, i.e., EEG data preprocessing, feature extraction and selection, classification method, and evaluation methods. Further, we review the commonly used deep learning methods for this domain. Finally, we expound on the open problem and future outlooks.

Automated summary

In this article, we surveyed literature on cognitive workload and machine learning techniques to identify approaches and highlight advances in the field. We introduced the concepts of cognitive workload and machine learning, and discussed the steps and methods used in classical machine learning and deep learning for cognitive workload recognition. The article also presents open problems and future outlooks in this domain.