ArticleBioengineering (Basel, Switzerland)2023
Prediction of Cognitive Load from Electroencephalography Signals Using Long Short-Term Memory Network.
Article in Bioengineering (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 55 citations in OpenAlex.
- Machine learning performance in EEG-based mental workload classification across task types: a systematic review.Frontiers in neuroergonomics · 2025Pooled it
- A Feasibility Study of Using an In-Ear EEG System for a Quantitative Assessment of Stress and Mental Workload.Sensors (Basel, Switzerland) · 2026Article
- ERP-based cognitive load decoding in middle-aged adults: effects of Alzheimer's risk.Medical & biological engineering & computing · 2026Article
- A neuroeducational approach to STEM education: Integrating real-time cognitive analysis and cognitive load theory to optimize teaching and learning at universities.Journal of education and health promotion · 2026Article
- Study on the effects of mental training on brain functional networks in operators during the glide phase of simulated carrier-based aircraft landing.Frontiers in psychology · 2026Article
- Article
- Cognitive load classification of mixed reality human computer interaction tasks based on multimodal sensor signals.Scientific reports · 2025Article
- Study on incentive mechanism of reward and punishment on work efficiency of PCB welder based on recurrence quantification analysis and electroencephalogram signals.Scientific reports · 2025Article
- Attachment Style, Task Difficulty, and Feedback Type: Effects on Cognitive Load.Behavioral sciences (Basel, Switzerland) · 2025Article
- An intelligent emotion prediction system using improved sand cat optimization technique based on EEG signals.Scientific reports · 2025Article
- From neurotechnology to the classroom: the promise of brain-computer interfaces for training systems engineers.Frontiers in human neuroscience · 2025Article
- Mobile eye-tracking and neuroimaging technologies reveal teaching and learning on the move: bibliometric mapping and content analysis.Psychoradiology · 2025Review
- Dual graph-embedded fusion network for predicting potential microbe-disease associations with sequence learning.Frontiers in genetics · 2025Article
- Research on low-power driving fatigue monitoring method based on spiking neural network.Experimental brain research · 2024Article
- Article
- Exploiting Information in Event-Related Brain Potentials from Average Temporal Waveform, Time-Frequency Representation, and Phase Dynamics.Bioengineering (Basel, Switzerland) · 2023Article
- Analysis of Relation between Brainwave Activity and Reaction Time of Short-Haul Pilots Based on EEG Data.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.