ITM Web Conf.
Volume 47, 20222022 2nd International Conference on Computer, Communication, Control, Automation and Robotics (CCCAR2022)
|Number of page(s)||10|
|Section||Algorithm Optimization and Application|
|Published online||23 June 2022|
Recognizing the level of organizational commitment based on deep learning methods and EEG
1 School of Artificial Intelligence, University of Chinese Academy of Sciences, China
2 Institute of Automation, Chinese Academy of Sciences, China
* Corresponding author: email@example.com
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.
Key words: Deep learning / EEG / Organizational commitment / Graph convolutional neural network
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.