基于脑电和眼动信号的人机交互意图识别

doi:10.7527/S1000-6893.2020.24290

电子电气工程与控制

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基于脑电和眼动信号的人机交互意图识别

王崴¹, 赵敏睿¹, 高虹霓¹, 朱帅¹, 瞿珏^1,2

1. 空军工程大学防空反导学院, 西安 710051;
2. 西北工业大学航空学院, 西安 710072

收稿日期:2020-05-26 修回日期:2020-06-15 发布日期:2020-07-17
通讯作者: 瞿珏 E-mail:qujue402@sina.com
基金资助:
国家自然科学基金（51675530）

Human-computer interaction: Intention recognition based on EEG and eye tracking

WANG Wei¹, ZHAO Minrui¹, GAO Hongni¹, ZHU Shuai¹, QU Jue^1,2

1. Air and Missile Defense College, Air Force Engineering University, Xi'an 710051, China;
2. School of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, China

Received:2020-05-26 Revised:2020-06-15 Published:2020-07-17
Supported by:
National Natural Science Foundation of China (51675530)

摘要/Abstract

摘要： 意图识别在人机交互（HCI）领域受到广泛关注，传统人机交互意图识别方法单纯依靠脑电（EEG）或眼动数据，不能很好地利用2种方法优点。为此，提出了一种融合脑电和眼动数据的人机交互意图识别方法，通过采集脑电和眼动信号，进行特征提取，输入机器学习模式识别网络进行意图识别，并基于Dempster-Shafer （D-S）证据理论进行决策层融合得出最终识别结果。招募了20名有效受试者进行交互意图识别实验，结果表明，基于脑电和眼动信号的人机交互意图识别方法识别准确率高于单纯依靠脑电和眼动数据的方法，可为下一步飞行器和武器系统人机交互系统自适应设计提供理论依据和技术支持。

关键词: 脑电, 眼动, 意图识别, 人机交互, 数据融合

Abstract: Intention recognition has received extensive attention in the field of Human-Computer Interaction (HCI). Traditional HCI intention recognition methods rely solely on an electroencephalogram (EEG) or eye movement data without making full use of the advantages of the two methods. This paper proposes an HCI intention recognition method that fuses EEG and eye movement data. It collects EEG and eye movement signals for feature extraction and inputs them into the network of machine learning and pattern recognition for intent recognition. Based on the Dempster-Shafer(D-S) evidence theory, the fusion of the decision layer is performed to obtain the final recognition result. In this study, 20 effective subjects are recruited for interactive intention recognition experiments. Results show that the recognition accuracy of the HCI intention recognition method based on EEG and eye movement signals is higher than that of traditional methods. Therefore, it can provide theoretical and technical support for the adaptive design of the HCI interface in aircraft and weapon equipment systems.

Key words: EEG, eye movement, intention recognition, human-computer interaction, data fusion

中图分类号:

V7
R857.14

王崴, 赵敏睿, 高虹霓, 朱帅, 瞿珏. 基于脑电和眼动信号的人机交互意图识别[J]. 航空学报, 2021, 42(2): 324290-324290.

WANG Wei, ZHAO Minrui, GAO Hongni, ZHU Shuai, QU Jue. Human-computer interaction: Intention recognition based on EEG and eye tracking[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021, 42(2): 324290-324290.

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E-mail：hkxb@buaa.edu.cn

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主管单位：中国科学技术协会主办单位：中国航空学会北京航空航天大学

基于脑电和眼动信号的人机交互意图识别

Human-computer interaction: Intention recognition based on EEG and eye tracking

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