Pattern Classification of Instantaneous Mental Workload Using Ensemble of Convolutional Neural Networks

In this paper, we consider the Mental Workload (MWL) classification problem based on the measured physiological data. Firstly we discussed the optimal classifier structure from two perspectives of Convolutional Neural Network (CNN) depth (i.e., the number of hidden layers) and parameter optimization algorithm. The base CNNs designed were tested according to Accuracy and the model training time required. Then we developed an Ensemble Convolutional Neural Network (ECNN) to enhance the robustness and accuracy of a single CNN model. For the ECNN design, three model aggregation approaches (weighted averaging, majority voting and stacking) were examined and a resampling strategy was used to enhance the diversity of individual CNN models. The results of a series of MWL classification performance comparison indicated that the proposed ECNN framework can effectively improve the MWL classification performance and is characterized by entirely automatic feature extraction and MWL classification, when compared with traditional machine learning methods.