Eeg-based tool for prediction of university students’ cognitive performance in the classroom

Mauricio A. Ramírez-Moreno, Mariana Díaz-Padilla, Karla D. Valenzuela-Gómez, Adriana Vargas-Martínez, Juan C. Tudón-Martínez, Rubén Morales-Menendez, Ricardo A. Ramírez-Mendoza, Blas L. Pérez-Henríquez, Jorge de J. Lozoya-Santos*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)


This study presents a neuroengineering-based machine learning tool developed to predict students’ performance under different learning modalities. Neuroengineering tools are used to predict the learning performance obtained through two different modalities: text and video. Electroencephalographic signals were recorded in the two groups during learning tasks, and performance was evaluated with tests. The results show the video group obtained a better performance than the text group. A correlation analysis was implemented to find the most relevant features to predict students’ performance, and to design the machine learning tool. This analysis showed a negative correlation between students’ performance and the (theta/alpha) ratio, and delta power, which are indicative of mental fatigue and drowsiness, respectively. These results indicate that users in a non-fatigued and well-rested state performed better during learning tasks. The designed tool obtained 85% precision at predicting learning performance, as well as correctly identifying the video group as the most efficient modality.

Original languageEnglish
Article number698
JournalBrain Sciences
Issue number6
Publication statusPublished - Jun 2021

Bibliographical note

Funding Information:
Funding: This research was funded partially by the NOVUS program of Tecnologico de Monterrey through grant number N19106.

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.

All Science Journal Classification (ASJC) codes

  • General Neuroscience


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