Cognitive load classification during online shopping using deep learning on time series eye movement indices

Cognitive load classification during online shopping activities is important to understand the user experience of e-commerce. Traditional classification methods that rely on proprietary software and obtrusive physiological measures often result in inconsistent performance. To address this research gap, we propose a novel approach that leverages deep learning to analyze raw eye movement data during online shopping tasks with low and high cognitive load. The Attention-based Long Short-Term Memory Fully Convolutional Network (ALSTM-FCN) model outperformed other machine learning and deep learning models with an average accuracy and F1 score of 97.70% and 97.69%, respectively. Cognitive load was also measured using the NASA TLX questionnaire, which showed significantly higher scores in high cognitive load tasks for all dimensions: “Mental Demand” (39.37, ), “Performance” (46.12, ), “Effort” (51.92, ), and “Frustration Level” (60.53, ). Based on the analysis of eye movement features used in cognitive load classification, we found that the variability in eye movement during tasks with low and high cognitive loads was predominantly spatial rather than temporal ( ). Our findings indicate a strong correlation between the deep learning-based classification of raw eye movement data and subjective cognitive load assessments. This study demonstrates the potential of using an affordable eye tracking sensor to classify cognitive load without being constrained by the capability of proprietary software.