ArticleScientific reports2024
Classification of mental workload using brain connectivity and machine learning on electroencephalogram data.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- MountPat: investigations on the EEG signals.Cognitive neurodynamics · 2026Article
- Benchmarking Multimodal Workload Classification: Effects of Modality, Validation Protocol, and Segmentation Contrast on an Open Graded-Arithmetic Dataset.Bioengineering (Basel, Switzerland) · 2026Article
- Different pattern: a new EEG-based method for mental performance detection.BMC medical informatics and decision making · 2026Article
- Connectivity-Based Pain Recognition from fNIRS: Parsimonious Subject-Independent Classification.Sensors (Basel, Switzerland) · 2026Article
- GACNet: A graph attention-based neural network for electroencephalography localization via contrastive learning.Scientific reports · 2026Article
- TensorCSBP: A Tensor Center-Symmetric Feature Extractor for EEG Odor Detection.Diagnostics (Basel, Switzerland) · 2026Article
- Assessment of cognitive load through photoplethysmography and bioimpedance responses during mental arithmetic tasks.Scientific reports · 2026Article
- Multivariate synchrosqueezing transform and time-frequency attention for mental workload classification from EEG signals.Scientific reports · 2026Article
- EEG-based brain-computer interface with immersive virtual reality for phantom limb pain: a single-center pilot neurofeedback trial.Frontiers in human neuroscience · 2026Article
- A low-latency neural inference framework for real-time handwriting recognition from EEG signals on an edge device.Scientific reports · 2025Article
- ACXNet hybrid deep learning model for cross task mental workload estimation using EEG neural manifolds.Scientific reports · 2025Article
- Integration of Multi-Modal Biosensing Approaches for Depression: Current Status, Challenges, and Future Perspectives.Sensors (Basel, Switzerland) · 2025Review
- Toward Wearable MagnetoCardioGraphy (MCG) for Cognitive Workload Monitoring: Advancements in Sensor and Study Design.Sensors (Basel, Switzerland) · 2025Article
- Fuzzy-Based Sensor Fusion for Cognitive Load Assessment in Inclusive Manufacturing Strategies.Sensors (Basel, Switzerland) · 2025Article
- Develop an emotion recognition system using jointly connectivity between electroencephalogram and electrocardiogram signals.Heliyon · 2025Article
- Cognitive load detection through EEG lead wise feature optimization and ensemble classification.Scientific reports · 2025Article
- Lobe-wise cognitive load detection using empirical Fourier decomposition and optimized machine learning.Frontiers in physiology · 2025Article
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Mental workload refers to the cognitive effort required to perform tasks, and it is an important factor in various fields, including system design, clinical medicine, and industrial applications. In this paper, we propose innovative methods to assess mental workload from EEG data that use effective brain connectivity for the purpose of extracting features, a hierarchical feature selection algorithm to select the most significant features, and finally machine learning models. We have used the Simultaneous Task EEG Workload (STEW) dataset, an open-access collection of raw EEG data from 48 subjects. We extracted brain-effective connectivities by the direct directed transfer function and then selected the top 30 connectivities for each standard frequency band. Then we applied three feature selection algorithms (forward feature selection, Relief-F, and minimum-redundancy-maximum-relevance) on the top 150 features from all frequencies. Finally, we applied sevenfold cross-validation on four machine learning models (support vector machine (SVM), linear discriminant analysis, random forest, and decision tree). The results revealed that SVM as the machine learning model and forward feature selection as the feature selection method work better than others and could classify the mental workload levels with accuracy equal to 89.53% (± 1.36).
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