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ORIGINAL RESEARCH article
Front. Neurosci.
Sec. Brain Imaging Methods
Volume 19 - 2025 | doi: 10.3389/fnins.2025.1499629
Knowledge Concept Recognition in the Learning Brain via fMRI Classification
Provisionally accepted- Northwestern Polytechnical University, Xi'an, China
Knowledge concept recognition (KCR) aims to identify the concepts learned in the brain, which has been a longstanding area of interest for learning science and education. While many studies have investigated object recognition using brain fMRIs, there are limited research on identifying specific knowledge points within the classroom. In this paper, we propose to recognize the knowledge concepts in computer science by classifying the brain fMRIs taken when students are learning the concepts. More specifically, this study made attempts on two representation strategies, i.e., voxel and time difference. Based on the representations, we evaluated traditional classifiers and the combination of CNN and LSTM for KCR. Experiments are conducted on a public dataset collected from 25 students and teachers in a computer science course. The evaluations of classifying fMRI segments show that the used classifiers all can attain a good performance when using the time-difference representation, where the CNN-LSTM model reaches the highest accuracy. This research contributes to the understanding of human learning and supports the development of personalized learning.
Keywords: Knowledge Concept Recognition, deep learning, fMRI classification, Brain identification, Learning Science Knowledge Concept Recognition
Received: 09 Oct 2024; Accepted: 25 Feb 2025.
Copyright: © 2025 Zhang, Zhang, Sun, Zhang and SHANG. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Yupei Zhang, Northwestern Polytechnical University, Xi'an, China
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