3D CNN based Multilevel Feature Fusion for Workload Estimation

Mental workload is defined as the proportion of the information processing capability used to perform a task. High cognitive load requires additional resources that may reduce the processing efficiency and performance. Therefore, the technique of workload estimation can ensure a proper working environment to promote the working efficiency of each person. In this paper, we propose a three-dimensional convolutional neural network (3D CNN) based a multilevel feature fusion algorithm for mental workload estimation using electroencephalogram (EEG) signals. Raw EEG signals are converted to 3D EEG images and then multilevel features are extracted in each layer of the 3D convolution operation and each multilevel feature is multiplied by a weighting factor, which determines the importance of the feature. The accuracy of our network is 90.3%, which is better than conventional algorithms.