A Miner Mental State Evaluation Scheme With Decision Level Fusion Based on Multidomain EEG Information

Abstract

It has been proven that electroencephalography (EEG) is an effective method for evaluating an individual's mental state. However, when it comes to the evaluation of miners' mental state, there are still some issues with missing EEG dataset and unsatisfactory evaluation accuracy. Therefore, this article proposes a miner mental state evaluation scheme with decision-level fusion based on multidomain EEG information. First, in the comprehensive lab for coal-related programs of Xi'an University of Science and Technology, the coal mine environment is simulated, and a realistic EEG dataset is constructed. Second, the multidomain features are extracted to represent abundant information in time, frequency, time-frequency, and space domain. These features with low dimension are classified adopting support vector machine (SVM), k-nearest neighbor (kNN), and back propagation (BP) network to obtain the optimal evaluation submodel (four domains corresponding to four submodels). Finally, based on the state probabilities provided by the optimal evaluation submodel, we adopt stack fusion and an improved Yager rule to fuse four submodels in order to find the most suitable fusion algorithm. The experimental results demonstrate that the average accuracy can reach 93.19% on the self-built dataset when utilizing the improved Yager rule with weight, and it realizes a better evaluation accuracy.