  Continuous assessment of task difficulty and mental workload is essential in
improving the usability and accessibility of interactive systems. Eye tracking
data has often been investigated to achieve this ability, with reports on the
limited role of standard blink metrics. Here, we propose a new approach to the
analysis of eye-blink responses for automated estimation of task difficulty.
The core module is a time-frequency representation of eye-blink, which aims to
capture the richness of information reflected on blinking. In our first study,
we show that this method significantly improves the sensitivity to task
difficulty. We then demonstrate how to form a framework where the represented
patterns are analyzed with multi-dimensional Long Short-Term Memory recurrent
neural networks for their non-linear mapping onto difficulty-related
parameters. This framework outperformed other methods that used hand-engineered
features. This approach works with any built-in camera, without requiring
specialized devices. We conclude by discussing how Rethinking Eye-blink can
benefit real-world applications.
