Prediction of Cognitive Load from Electroencephalography Signals Using Long Short-Term Memory Network

Gilsang Yoo, Hyeoncheol Kim, Sungdae Hong

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


In recent years, the development of adaptive models to tailor instructional content to learners by measuring their cognitive load has become a topic of active research. Brain fog, also known as confusion, is a common cause of poor performance, and real-time detection of confusion is a challenging and important task for applications in online education and driver fatigue detection. In this study, we propose a deep learning method for cognitive load recognition based on electroencephalography (EEG) signals using a long short-term memory network (LSTM) with an attention mechanism. We obtained EEG signal data from a database of brainwave information and associated data on mental load. We evaluated the performance of the proposed LSTM technique in comparison with random forest, Adaptive Boosting (AdaBoost), support vector machine, eXtreme Gradient Boosting (XGBoost), and artificial neural network models. The experimental results demonstrated that the proposed approach had the highest accuracy of 87.1% compared to those of other algorithms, including random forest (64%), AdaBoost (64.31%), support vector machine (60.9%), XGBoost (67.3%), and artificial neural network models (71.4%). The results of this study support the development of a personalized adaptive learning system designed to measure and actively respond to learners’ cognitive load in real time using wireless portable EEG systems.

Original languageEnglish
Article number361
Issue number3
Publication statusPublished - 2023 Mar

Bibliographical note

Funding Information:
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (2020R1I1A1A01064580).

Publisher Copyright:
© 2023 by the authors.


  • attention mechanism
  • cognitive load
  • deep learning
  • electroencephalography
  • long short-term memory network

ASJC Scopus subject areas

  • Bioengineering


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