Systematic Review of Experimental Paradigms and Deep Neural Networks for Electroencephalography-Based Cognitive Workload Detection

TL;DR

This systematic review evaluates EEG-based cognitive workload detection, highlighting experimental paradigms and deep neural network input representations, emphasizing the need for interpretable models for real-time applications.

Contribution

The paper provides a comprehensive overview of EEG paradigms and DNN input structures, advocating for more interpretable models in cognitive workload estimation.

Findings

Few studies use online or pseudo-online classification for real-time CWL.

Most DNNs are used as black-box models without interpretability.

DNNs are effective due to their deep architecture's modeling power.

Abstract

This article summarizes a systematic review of the electroencephalography (EEG)-based cognitive workload (CWL) estimation. The focus of the article is twofold: identify the disparate experimental paradigms used for reliably eliciting discreet and quantifiable levels of cognitive load and the specific nature and representational structure of the commonly used input formulations in deep neural networks (DNNs) used for signal classification. The analysis revealed a number of studies using EEG signals in its native representation of a two-dimensional matrix for offline classification of CWL. However, only a few studies adopted an online or pseudo-online classification strategy for real-time CWL estimation. Further, only a couple of interpretable DNNs and a single generative model were employed for cognitive load detection till date during this review. More often than not, researchers were using DNNs as black-box type models. In conclusion, DNNs prove to be valuable tools for classifying EEG signals, primarily due to the substantial modeling power provided by the depth of their network architecture. It is further suggested that interpretable and explainable DNN models must be employed for cognitive workload estimation since existing methods are limited in the face of the non-stationary nature of the signal.