[1]TONG Qingyun,GONG Peiliang,ZHANG Liying,et al.Temporal attention memory network for evaluating air traffic controller cognitive load[J].CAAI Transactions on Intelligent Systems,2026,21(3):792-801.[doi:10.11992/tis.202507018]
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Temporal attention memory network for evaluating air traffic controller cognitive load

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 21 Number of periods: 2026 3 Page number: 792-801 Column: 人工智能院长论坛 Public date: 2026-05-05
Title:
Temporal attention memory network for evaluating air traffic controller cognitive load
Author(s):
TONG Qingyun1; 2;  GONG Peiliang1; 2;  ZHANG Liying1; 2;  WANG Kun1; 2;  ZHOU Yueying3;  ZHANG Daoqiang1; 2
1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China;
2. The Key Laboratory of Brain-Machine Intelligence Technology (Nanjing University of Aeronautics and Astronautics), Ministry of Education, Nanjing 211106, China;
3. School of Mathematics Science, Liaocheng University, Liaocheng 252000, China
Keywords:
air traffic control; electroencephalography; time series analysis; attention mechanism; cognitive load evaluation; neural network; long short-term memory network; classification recognition; human-computer interaction
CLC:
TP391
DOI:
10.11992/tis.202507018
Abstract:
To improve the accuracy and real-time performance of cognitive state monitoring in complex work scenarios, this paper focuses on cognitive load recognition in air traffic control tasks and proposes a temporal attention memory network (TAM-Net). The method models the dynamic features of electroencephalography (EEG) signals by integrating a surrogate attention mechanism and attentional long short-term memory (LSTM) units. The surrogate attention mechanism effectively reduces computational overhead while maintaining global modeling capabilities through surrogate tokens, and the attentional LSTM units enhance the model’s capacity for memory and representation of long sequences and complex dependencies. Experiments were conducted on a self-collected EEG dataset, and the results show that TAM-Net considerably outperforms comparative models in two types of tasks. These findings indicate that TAM-Net can effectively capture complex temporal dependency features, providing a new methodology and technical support for precise cognitive load monitoring and real-time regulation.
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