  Classifying human cognitive states from behavioral and physiological signals
is a challenging problem with important applications in robotics. The problem
is challenging due to the data variability among individual users, and sensor
artefacts. In this work, we propose an end-to-end framework for real-time
cognitive workload classification with mixture Hyper Long Short Term Memory
Networks, a novel variant of HyperNetworks. Evaluating the proposed approach on
an eye-gaze pattern dataset collected from simulated driving scenarios of
different cognitive demands, we show that the proposed framework outperforms
previous baseline methods and achieves 83.9\% precision and 87.8\% recall
during test. We also demonstrate the merit of our proposed architecture by
showing improved performance over other LSTM-based methods.
