  Task engagement is defined as loadings on energetic arousal (affect), task
motivation, and concentration (cognition). It is usually challenging and
expensive to label cognitive state data, and traditional computational models
trained with limited label information for engagement assessment do not perform
well because of overfitting. In this paper, we proposed two deep models (i.e.,
a deep classifier and a deep autoencoder) for engagement assessment with scarce
label information. We recruited 15 pilots to conduct a 4-h flight simulation
from Seattle to Chicago and recorded their electroencephalograph (EEG) signals
during the simulation. Experts carefully examined the EEG signals and labeled
20 min of the EEG data for each pilot. The EEG signals were preprocessed and
power spectral features were extracted. The deep models were pretrained by the
unlabeled data and were fine-tuned by a different proportion of the labeled
data (top 1%, 3%, 5%, 10%, 15%, and 20%) to learn new representations for
engagement assessment. The models were then tested on the remaining labeled
data. We compared performances of the new data representations with the
original EEG features for engagement assessment. Experimental results show that
the representations learned by the deep models yielded better accuracies for
the six scenarios (77.09%, 80.45%, 83.32%, 85.74%, 85.78%, and 86.52%), based
on different proportions of the labeled data for training, as compared with the
corresponding accuracies (62.73%, 67.19%, 73.38%, 79.18%, 81.47%, and 84.92%)
achieved by the original EEG features. Deep models are effective for engagement
assessment especially when less label information was used for training.
