Fuzzy Integral Optimization with Deep Q-Network for EEG-Based Intention Recognition

2018 ◽  
pp. 156-168 ◽  
Author(s):  
Dalin Zhang ◽  
Lina Yao ◽  
Sen Wang ◽  
Kaixuan Chen ◽  
Zheng Yang ◽  
...  
Keyword(s):  
Fuzzy Integral ◽  
Intention Recognition

Effect of Visualization of Pedestrian Intention Recognition on Trust and Cognitive Load

2020 ◽  
Author(s):  
Mark Colley ◽  
Christian Bräuner ◽  
Mirjam Lanzer ◽  
Marcel Walch ◽  
Martin Baumann ◽  
...  
Keyword(s):  
Cognitive Load ◽  
Intention Recognition

Ergonomics analysis based on intention inference

2021 ◽  
pp. 1-16
Author(s):  
First A. Wenbo Huang ◽  
Second B. Changyuan Wang ◽  
Third C. Hongbo Jia
Keyword(s):  
Eye Movement ◽  
Recognition Rate ◽  
Tactile Feedback ◽  
Movement Direction ◽  
Support Vector ◽  
Eeg Signals ◽  
Intention Recognition ◽  
Baseline Removal ◽  
Inference Methods ◽  
Intention Inference

Traditional intention inference methods rely solely on EEG, eye movement or tactile feedback, and the recognition rate is low. To improve the accuracy of a pilot’s intention recognition, a human-computer interaction intention inference method is proposed in this paper with the fusion of EEG, eye movement and tactile feedback. Firstly, EEG signals are collected near the frontal lobe of the human brain to extract features, which includes eight channels, i.e., AF7, F7, FT7, T7, AF8, F8, FT8, and T8. Secondly, the signal datas are preprocessed by baseline removal, normalization, and least-squares noise reduction. Thirdly, the support vector machine (SVM) is applied to carry out multiple binary classifications of the eye movement direction. Finally, the 8-direction recognition of the eye movement direction is realized through data fusion. Experimental results have shown that the accuracy of classification with the proposed method can reach 75.77%, 76.7%, 83.38%, 83.64%, 60.49%,60.93%, 66.03% and 64.49%, respectively. Compared with traditional methods, the classification accuracy and the realization process of the proposed algorithm are higher and simpler. The feasibility and effectiveness of EEG signals are further verified to identify eye movement directions for intention recognition.

Tactical Intention Recognition in Wargame

2021 ◽  
Author(s):  
Xuan Liu ◽  
Meijing Zhao ◽  
Song Dai ◽  
Qiyue Yin ◽  
Wancheng Ni
Keyword(s):  
Intention Recognition
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