ArticleFrontiers in neuroinformatics2022
An Evaluation of the EEG Alpha-to-Theta and Theta-to-Alpha Band Ratios as Indexes of Mental Workload.
Article in Frontiers in neuroinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 3 of them syntheses that pooled it.
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Who cites it
33 citing papers in PubMed, 3 syntheses or guidelines pooled it, 103 citations in OpenAlex.
- An Overview of Stress Analysis Based on Physiological Signals: Systematic Review of Open Datasets and Current Trends.Sensors (Basel, Switzerland) · 2025Pooled it
- Human-Centric Cognitive State Recognition Using Physiological Signals: A Systematic Review of Machine Learning Strategies Across Application Domains.Sensors (Basel, Switzerland) · 2025Pooled it
- Machine learning performance in EEG-based mental workload classification across task types: a systematic review.Frontiers in neuroergonomics · 2025Pooled it
- Toward Personalized Surgeons' Cognitive Workload: A Linear Mixed-Effects Model Analysis of EEG Features.IFMBE proceedings · 2027Article
- Make it fun but keep it simple: EEG reveals the impact of easy yet engaging games for stroke rehabilitation.Journal of neuroengineering and rehabilitation · 2026Article
- Mapping the Digital Mind: A Meta-Analysis of EEG Biomarkers in Cognition, Emotion, and Mental Health.Brain sciences · 2026Review
- Improving cognitive stress classification via multimodal EEG and ECG fusion: gender differences in physiological response.Scientific reports · 2026Article
- Deception Detection from Five-Channel Wearable EEG on LieWaves: A Reproducible Baseline for Subject-Dependent and Subject-Independent Evaluation.Sensors (Basel, Switzerland) · 2026Article
- A Feasibility Study of Using an In-Ear EEG System for a Quantitative Assessment of Stress and Mental Workload.Sensors (Basel, Switzerland) · 2026Article
- A novel approach for the EEG-driven assessment of divided attention through mutual information theory: A case study at the wheel.PloS one · 2026Article
- From signal to construct evidence: multimodal consumer-wearable sensing in real general-aviation flight.Frontiers in neuroergonomics · 2026Article
- Mental fatigue in football: methodology and experimental protocol.Frontiers in sports and active living · 2026Article
- Discrete wavelet transform-driven optimized deep learning-based framework for dyslexia detection using EEG signals.Frontiers in neuroinformatics · 2026Article
- Integrating memory-guided saccades and EEG for MCI screening: a multimodal LASSO modeling approach.Frontiers in aging neuroscience · 2026Article
- Engagement and cognitive load of upper-year medical trainees during mixed reality-enhanced dissection.Anatomical sciences education · 2025Article
- Cognitive efficiency in VR simulated natural indoor environments examined through EEG and affective responses.Scientific reports · 2025Article
- Educational multimedia design principles affect local and global information processing in functional brain networks.Scientific reports · 2025Article
- Task-Independent Cognitive Workload Discrimination Based on EEG with Stacked Graph Attention Convolutional Networks.Sensors (Basel, Switzerland) · 2025Article
- Leveraging neuroinformatics to understand cognitive phenotypes in elite athletes through systems neuroscience.Frontiers in neuroinformatics · 2025Article
- Resting state electroencephalography alpha activity predicts cognitive scores in rapid eye movement sleep behavior disorder.Sleep advances : a journal of the Sleep Research Society · 2025Article
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2 authors at 1 institution in 1 country.
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Abstract
Many research works indicate that EEG bands, specifically the alpha and theta bands, have been potentially helpful cognitive load indicators. However, minimal research exists to validate this claim. This study aims to assess and analyze the impact of the alpha-to-theta and the theta-to-alpha band ratios on supporting the creation of models capable of discriminating self-reported perceptions of mental workload. A dataset of raw EEG data was utilized in which 48 subjects performed a resting activity and an induced task demanding exercise in the form of a multitasking SIMKAP test. Band ratios were devised from frontal and parietal electrode clusters. Building and model testing was done with high-level independent features from the frequency and temporal domains extracted from the computed ratios over time. Target features for model training were extracted from the subjective ratings collected after resting and task demand activities. Models were built by employing Logistic Regression, Support Vector Machines and Decision Trees and were evaluated with performance measures including accuracy, recall, precision and f1-score. The results indicate high classification accuracy of those models trained with the high-level features extracted from the alpha-to-theta ratios and theta-to-alpha ratios. Preliminary results also show that models trained with logistic regression and support vector machines can accurately classify self-reported perceptions of mental workload. This research contributes to the body of knowledge by demonstrating the richness of the information in the temporal, spectral and statistical domains extracted from the alpha-to-theta and theta-to-alpha EEG band ratios for the discrimination of self-reported perceptions of mental workload.
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