Knowledge Commons of Institute of Automation,CAS
Recognizing the Level of Organizational Commitment Based on Deep Learning Methods and EEG | |
Zhang R(张睿)1,2; Wang ZY(王子洋)2; Yang FM(杨芳梅)2; Liu Y(刘禹)2 | |
2022 | |
会议名称 | 2022 2nd International Conference on Computer, Communication, Control, Automation and Robotics (CCCAR2022) |
会议日期 | 2022-4-30 |
会议地点 | 中国 上海 |
摘要 | 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. |
语种 | 英语 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/48530 |
专题 | 多模态人工智能系统全国重点实验室_脑机融合与认知评估 |
作者单位 | 1.中国科学院大学 2.中国科学院自动化研究所 |
第一作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Zhang R,Wang ZY,Yang FM,et al. Recognizing the Level of Organizational Commitment Based on Deep Learning Methods and EEG[C],2022. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Recognizing the Leve(805KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论