"Cognitector2024"

"Attention monitoring via facial expressions has relied on unimodal datasets, which lack the context and task-related information for harnessing emotions, suffer from imbalance and exhibit annotation biases. By context-aware, emotions of individuals elicited during the data gathering, however, had no connection with any cognitive processing. Predominantly, the Dataset for Affective States in E-Environments (DAiSEE) dataset has been adopted as a common dataset for most studies on attention and engagement, though it has been employed in machine learning and deep learning related studies for engagement or attention monitoring. In this paper, we present the first facial-expression dataset explicitly designed for monitoring based on the proven cognitive experiment, the trail-making test(TMT)-A. TMT-A is a standard cognitive task that directly correlates with attention and executive function. Experiments with convolutional neural network-based and vision-transformer models were conducted on the new datasets, as the number of participants was only 43. There were two classes chosen as average and fast attention rates.  Comparison with other open-source datasets which are connected to attention monitoring was presented."