Classification of Cognitive Load Using Deep Learning Based on Eye Movement Indices

Abstract

The classification of cognitive load is crucial to evaluate mental effort in various tasks. Compared to physiological measures such as electroencephalography (EEG), electrocardiography (ECG), or galvanic skin response (GSR), eye tracking offers a less intrusive method for the classification of cognitive load. However, previous studies that used traditional machine learning faced limitations in the accuracy of multiclass classification and relied on proprietary feature extraction methods. Hence, it hinders reproducibility with noncommercial software. To address these challenges, we propose a novel approach by implementing three time series deep learning models—Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Temporal Convolutional Network (TCN)—to enhance cognitive load classification in multiclass scenarios. By introducing various temporal windows for novel features and optimizing hyperparameters during training, we improved the accuracy of cognitive load classification. We benchmarked these deep learning models against traditional machine learning techniques on the COLET dataset. The experimental results revealed that deep learning models significantly outperformed conventional machine learning methods, with BiLSTM achieving the highest accuracies of 0.8780 and 0.8836 for the classification of cognitive load and activity tasks in multiclass scenarios, respectively. This study highlights the potential of deep learning to revolutionize cognitive workload assessments. Using eye movement indices, our method facilitates accurate, scalable, and cost-effective solutions suitable for low-cost eye tracking devices, broadening accessibility for applications in education, healthcare, and beyond.