ArticleSensors (Basel, Switzerland)2020
Pattern Recognition of Cognitive Load Using EEG and ECG Signals.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 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
- Benchmarking Multimodal Workload Classification: Effects of Modality, Validation Protocol, and Segmentation Contrast on an Open Graded-Arithmetic Dataset.Bioengineering (Basel, Switzerland) · 2026Article
- Functional network activation during a subtraction task.Scientific reports · 2026Article
- The Neuro-Cardiac Symbiotic Engine: A Multimodal Fusion Architecture for Cognitive State Decoding via High-Performance Computing.Life (Basel, Switzerland) · 2026Article
- Improving cognitive stress classification via multimodal EEG and ECG fusion: gender differences in physiological response.Scientific reports · 2026Article
- Processing and analysis of portable EEG data for cognitive load assessment in neurotypical university students.Frontiers in human neuroscience · 2026Article
- 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
- A Pilot Study on Video Game Training Effects on Visual Working Memory: Behavioral and Neural Insights.Brain sciences · 2025Article
- A cross-attention swin transformer network for EEG-based subject-independent cognitive load assessment.Cognitive neurodynamics · 2024Article
- Machine learning algorithms for detection of visuomotor neural control differences in individuals with PASC and ME.Frontiers in human neuroscience · 2024Article
- Reliability of Mental Workload Index Assessed by EEG with Different Electrode Configurations and Signal Pre-Processing Pipelines.Sensors (Basel, Switzerland) · 2023Article
- Feature Selection for Continuous within- and Cross-User EEG-Based Emotion Recognition.Sensors (Basel, Switzerland) · 2022Article
- Modeling Cognitive Load as a Self-Supervised Brain Rate with Electroencephalography and Deep Learning.Brain sciences · 2022Article
- Assessment of mental fatigue and stress on electronic sport players with data fusion.Medical & biological engineering & computing · 2021Article
- Emotion Recognition Based on Skin Potential Signals with a Portable Wireless Device.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
The matching of cognitive load and working memory is the key for effective learning, and cognitive effort in the learning process has nervous responses which can be quantified in various physiological parameters. Therefore, it is meaningful to explore automatic cognitive load pattern recognition by using physiological measures. Firstly, this work extracted 33 commonly used physiological features to quantify autonomic and central nervous activities. Secondly, we selected a critical feature subset for cognitive load recognition by sequential backward selection and particle swarm optimization algorithms. Finally, pattern recognition models of cognitive load conditions were constructed by a performance comparison of several classifiers. We grouped the samples in an open dataset to form two binary classification problems: (1) cognitive load state vs. baseline state; (2) cognitive load mismatching state vs. cognitive load matching state. The decision tree classifier obtained 96.3% accuracy for the cognitive load vs. baseline classification, and the support vector machine obtained 97.2% accuracy for the cognitive load mismatching vs. cognitive load matching classification. The cognitive load and baseline states are distinguishable in the level of active state of mind and three activity features of the autonomic nervous system. The cognitive load mismatching and matching states are distinguishable in the level of active state of mind and two activity features of the autonomic nervous system.
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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.