Through this paper, we introduce a novel driver cognitive load assessment dataset, CL-Drive, which contains Electroencephalogram (EEG) signals along with other physiological signals such as Electrocardiography (ECG) and Electrodermal Activity (EDA) as well as eye tracking data. The data was collected from 21 subjects while driving in an immersive vehicle simulator, in various driving conditions, to induce different levels of cognitive load in the subjects. The tasks consisted of 9 complexity levels for 3 minutes each. Each driver reported their subjective cognitive load every 10 seconds throughout the experiment. The dataset contains the subjective cognitive load recorded as ground truth. In this paper, we also provide benchmark classification results for different machine learning and deep learning models for both binary and ternary label distributions. We followed 2 evaluation criteria namely 10-fold and leave-one-subject-out (LOSO). We have trained our models on both hand-crafted features as well as on raw data.

翻译：通过本文，我们介绍了一个全新的驾驶员认知负荷评估数据集CL-Drive，该数据集包含脑电图（EEG）信号，以及心电图（ECG）、皮肤电活动（EDA）等其他生理信号，同时还包括眼动追踪数据。数据采集自21名受试者在沉浸式车辆模拟器中驾驶时的不同驾驶条件，以诱发受试者不同水平的认知负荷。任务包含9个复杂度层级，每个层级持续3分钟。在实验过程中，每位驾驶员每10秒报告一次主观认知负荷。本数据集将记录的主观认知负荷作为地面真值。本文还提供了不同机器学习与深度学习模型在二分类及三分类标签分布下的基准分类结果。我们遵循两种评估标准，即10折交叉验证与留一受试者法（LOSO）。模型训练基于手工提取特征及原始数据两种方式。