ArticleFrontiers in neuroscience2022
Assessment of instantaneous cognitive load imposed by educational multimedia using electroencephalography signals.
Article in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning performance in EEG-based mental workload classification across task types: a systematic review.Frontiers in neuroergonomics · 2025Pooled it
- Optimization of attention mechanisms in choral conducting education using EEG and eye movement tracking with transfer learning algorithms.Scientific reports · 2026Article
- Processing and analysis of portable EEG data for cognitive load assessment in neurotypical university students.Frontiers in human neuroscience · 2026Article
- Discriminative Capabilities of Eye Gaze Measures for Cognitive Load Evaluation in a Driving Simulation Task.Journal of eye movement research · 2025Article
- EEG analysis of brain dynamics in a simulated multi-task and multi-stage learning environment.NPJ science of learning · 2025Article
- Educational multimedia design principles affect local and global information processing in functional brain networks.Scientific reports · 2025Article
- Article
- Cognitive load and visual attention assessment using physiological eye tracking measures in multimedia learning.PloS one · 2025Article
- Effects and prediction of cognitive load on encoding model of brain response to auditory and linguistic stimuli in educational multimedia.Scientific reports · 2024Article
- Reliability of Mental Workload Index Assessed by EEG with Different Electrode Configurations and Signal Pre-Processing Pipelines.Sensors (Basel, Switzerland) · 2023Article
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5 authors.
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Abstract
The use of multimedia learning is increasing in modern education. On the other hand, it is crucial to design multimedia contents that impose an optimal amount of cognitive load, which leads to efficient learning. Objective assessment of instantaneous cognitive load plays a critical role in educational design quality evaluation. Electroencephalography (EEG) has been considered a potential candidate for cognitive load assessment among neurophysiological methods. In this study, we experiment to collect EEG signals during a multimedia learning task and then build a model for instantaneous cognitive load measurement. In the experiment, we designed four educational multimedia in two categories to impose different levels of cognitive load by intentionally applying/violating Mayer's multimedia design principles. Thirty university students with homogenous English language proficiency participated in our experiment. We divided them randomly into two groups, and each watched a version of the multimedia followed by a recall test task and filling out a NASA-TLX questionnaire. EEG signals are collected during these tasks. To construct the load assessment model, at first, power spectral density (PSD) based features are extracted from EEG signals. Using the minimum redundancy - maximum relevance (MRMR) feature selection approach, the best features are selected. In this way, the selected features consist of only about 12% of the total number of features. In the next step, we propose a scoring model using a support vector machine (SVM) for instantaneous cognitive load assessment in 3s segments of multimedia. Our experiments indicate that the selected feature set can classify the instantaneous cognitive load with an accuracy of 84.5 ± 2.1%. The findings of this study indicate that EEG signals can be used as an appropriate tool for measuring the cognitive load introduced by educational videos. This can be help instructional designers to develop more effective content.
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