Recognizing the level of organizational commitment based on deep learning methods and EEG

Abstract

In recent years, the application scenarios for Electroencephalogram (EEG) research have become increasingly extensive. Compared to other tasks, using EEG to recognize the difference in the levels of subjects’ personality traits is a greater challenge to some extent. In this paper, we propose a new task of recognizing the level of people’s Organizational Commitment based on EEG signals and Deep Learning methods. Aiming at this goal, we constructed a graph convolutional neural network structure (EEG-GCN) based on the topological graph of EEG features, and compared it with other deep learning model frameworks such as one-dimensional convolutional neural network (1D-CNN), two-dimensional convolutional neural network (2D-CNN), and LSTM. Meanwhile, we have studied the construction of the adjacency matrix of the EEG feature topology map, and finally found that the combination of Pairwise Phase Consistency (PPC) and geodetic distance is the best choice. The model we constructed can achieve an average accuracy of 79.1%. Furthermore, after expanding the size of our dataset, our model is able to achieve an overall average accuracy of 81.9%. Therefore, it can be seen that the combination of resting-state EEG and deep learning method is effective in recognizing organizational commitment personality traits.