Deep Learning Architectures Used In Eeg-Based Estimation Of Cognitive workload: A Review

Cognitive workload (CWL) refers to the ratio of a participant’s mental effort over his/her brain capacity when executing tasks with aid of a machine. Such CWL influences the participant’s trust placed on the machine and thus affects the tasks’ performance. Efficient human-machine interaction demands the machine’s real-time adaptation to meet an admissible CWL for the participant. The adaptation needs estimating CWL based on brain activities captured by non-invasive electroencephalography (EEG). Since deep learning (DL) is common for extracting EEG features reflecting certain characteristics of the activities, DL-based CWL estimation attracts ample attention. Herein, we present a review to summarize current trends in DL architectures for EEG-based CWL estimation and to identify gaps in the trends for future work.