Nonlinear Dynamic Classification of Momentary Mental Workload Using Physiological Features and NARX-Model-Based Least-Squares Support Vector Machines

This paper designs a pattern classifier based on a Nonlinear AutoRegressive model with eXogenous inputs (NARX) to reveal intricate nonlinear dynamical correlation between mental workload (MWL) of a human operator and psychophysiological features. The salient electroencephalogram and electrocardiogram features were selected as inputs to the NARX model, whose continuous output was discretized in terms of five MWL classes at each time instant. The orders of the NARX model were determined using an objective function to achieve a good tradeoff between model accuracy and complexity via a least-squares support vector machine. The physiological features from different measurement channels (electrodes) and frequency bands were compared in terms of multiclass MWL classification performance. The classification results showed that the locality projection preservation technique can maintain sufficiently high MWL classification accuracy (with the highest five-class correct classification rate of 88%) with a significantly reduced computational complexity. The comparative results of classification performance also demonstrated the superiority of the proposed dynamic model to a widely-used static model.