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Navigation Learning Assessment Using EEG-Based Multi-Time Scale Spatiotemporal Compound Model.

Authors :
Wang L
Liu Y
Li Y
Chen R
Liu X
Fu L
Wang Y
Source :
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society [IEEE Trans Neural Syst Rehabil Eng] 2024; Vol. 32, pp. 537-547. Date of Electronic Publication: 2024 Jan 29.
Publication Year :
2024

Abstract

This study presents a novel method to assess the learning effectiveness using Electroencephalography (EEG)-based deep learning model. It is difficult to assess the learning effectiveness of professional courses in cultivating students' ability objectively by questionnaire or other assessment methods. Research in the field of brain has shown that innovation ability can be reflected from cognitive ability which can be embodied by EEG signal features. Three navigation tasks of increasing cognitive difficulty were designed and a total of 41 subjects participated in the experiment. For the classification and tracking of the subjects' EEG signals, a convolutional neural network (CNN)-based Multi-Time Scale Spatiotemporal Compound Model (MTSC) is proposed in this paper to extract and classify the features of the subjects' EEG signals. Furthermore, Spiking neural networks (SNN) -based NeuCube is used to assess the learning effectiveness and demonstrate cognitive processes, acknowledging that NeuCube is an excellent method to display the spatiotemporal differences between spikes emitted by neurons. The results of the classification experiment show that the cognitive training traces of different students in solving three navigational problems can be effectively distinguished. More importantly, new information about navigation is revealed through the analysis of feature vector visualization and model dynamics. This work provides a foundation for future research on cognitive navigation and the training of students' navigational skills.

Details

Language :
English
ISSN :
1558-0210
Volume :
32
Database :
MEDLINE
Journal :
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Publication Type :
Academic Journal
Accession number :
38145526
Full Text :
https://doi.org/10.1109/TNSRE.2023.3346766