Pattern Recognition of Momentary Mental Workload Based on Multi-Channel Electrophysiological Data and Ensemble Convolutional Neural Networks - Zhang, Jianhua; Li, Sunan; Wang, Rubin (Frontiers in Neuroscience, 2017)
In this paper, we consider the Mental Workload (MWL) classification problem based on the measured physiological data. First we discussed the optimal depth (i.e., the number of hidden layers) and parameter optimization algorithms for the Convolutional Neural Networks (CNN). The base CNNs designed were tested according to five classification performance indices, Accuracy, Precision, F-measure, G-mean and required training time. 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 MWL classification performance comparisons indicated that the proposed ECNN framework can effectively improve MWL classification performance and is characterized by entirely automatic feature extraction and MWL classification, when compared with traditional machine learning methods.