  We present a novel multimodal dataset for Cognitive Load Assessment in
REaltime (CLARE). The dataset contains physiological and gaze data from 24
participants with self-reported cognitive load scores as ground-truth labels.
The dataset consists of four modalities, namely, Electrocardiography (ECG),
Electrodermal Activity (EDA), Electroencephalogram (EEG), and Gaze tracking. To
map diverse levels of mental load on participants during experiments, each
participant completed four nine-minutes sessions on a computer-based operator
performance and mental workload task (the MATB-II software) with varying levels
of complexity in one minute segments. During the experiment, participants
reported their cognitive load every 10 seconds. For the dataset, we also
provide benchmark binary classification results with machine learning and deep
learning models on two different evaluation schemes, namely, 10-fold and
leave-one-subject-out (LOSO) cross-validation. Benchmark results show that for
10-fold evaluation, the convolutional neural network (CNN) based deep learning
model achieves the best classification performance with ECG, EDA, and Gaze. In
contrast, for LOSO, the best performance is achieved by the deep learning model
with ECG, EDA, and EEG.
