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.

通过系统综述了解了基于脑电图 (EEG) 的认知负荷估计。重点包括：确定可靠引发离散和量化认知负荷水平的实验范式，并探究在信号分类中常用的深度神经网络 (DNNs) 的输入形式的特点和表征结构。分析发现一些研究在离线认知负荷分类中使用脑电信号的二维矩阵原始表示，但只有少数研究采用了在线或伪在线的实时认知负荷估计策略。此外，在这项综述中，仅采用了数个可解释的 DNNs 和一个生成模型进行认知负荷检测。总的来说，由于其网络架构的深度提供了巨大的建模能力，DNNs 成为分类脑电信号的宝贵工具。进一步建议采用可解释且可解读的 DNN 模型进行认知负荷估计，因为现有方法在面对非平稳信号时存在局限性。