  In this paper, we present a new feature that can be used to classify
cognitive load based on pupil information. The feature consists of a temporal
segmentation of the eye tracking recordings. For each segment of the temporal
partition, a probability distribution of pupil size is computed and stored.
These probability distributions can then be used to classify the cognitive
load. The presented feature significantly improves the classification accuracy
of the cognitive load compared to other statistical values obtained from eye
tracking data, which represent the state of the art in this field. The
applications of determining Cognitive Load from pupil data are numerous and
could lead, for example, to pre-warning systems for burnouts.
  Link:
https://es-cloud.cs.uni-tuebingen.de/d/8e2ab8c3fdd444e1a135/?p=%2FCognitiveLoadFeature&mode=list
