CLARE: Cognitive Load Assessment in REaltime with Multimodal Data

TL;DR

This paper introduces CLARE, a comprehensive multimodal dataset for real-time cognitive load assessment using physiological and gaze data, along with benchmark machine learning results demonstrating its potential for workload classification.

Contribution

The paper provides the first multimodal dataset with synchronized physiological and gaze data for real-time cognitive load assessment, including benchmark results with deep learning models.

Findings

CNN achieves best performance with ECG, EDA, Gaze in 10-fold evaluation.

Deep learning with ECG, EDA, EEG performs best in LOSO evaluation.

Benchmark results establish baseline performance for future research.

Abstract

We present a novel multimodal dataset for Cognitive Load Assessment in REal-time (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.