ArticleSensors (Basel, Switzerland)2020
Feature Selection Model based on EEG Signals for Assessing the Cognitive Workload in Drivers.
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 9 papers, 2 of them syntheses that pooled it.
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Who cites it
9 citing papers in PubMed, 2 syntheses or guidelines pooled 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
- Decoding visual object recognition from EEG signals.PloS one · 2026Article
- Article
- Reproducible machine learning research in mental workload classification using EEG.Frontiers in neuroergonomics · 2024Review
- Machine learning-based cognitive load prediction model for AR-HUD to improve OSH of professional drivers.Frontiers in public health · 2023Article
- On the Improvement of Eye Tracking-Based Cognitive Workload Estimation Using Aggregation Functions.Sensors (Basel, Switzerland) · 2021Article
- Modern Trends and Applications of Intelligent Methods in Biomedical Signal and Image Processing.Sensors (Basel, Switzerland) · 2021Article
- Research on Mental Stress Recognition of Depressive Disorders in Patients With Androgenic Alopecia Based on Machine Learning and Fuzzy K-Means Clustering.Frontiers in genetics · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, research has focused on generating mechanisms to assess the levels of subjects' cognitive workload when performing various activities that demand high concentration levels, such as driving a vehicle. These mechanisms have implemented several tools for analyzing the cognitive workload, and electroencephalographic (EEG) signals have been most frequently used due to their high precision. However, one of the main challenges in implementing the EEG signals is finding appropriate information for identifying cognitive states. Here, we present a new feature selection model for pattern recognition using information from EEG signals based on machine learning techniques called GALoRIS. GALoRIS combines Genetic Algorithms and Logistic Regression to create a new fitness function that identifies and selects the critical EEG features that contribute to recognizing high and low cognitive workloads and structures a new dataset capable of optimizing the model's predictive process. We found that GALoRIS identifies data related to high and low cognitive workloads of subjects while driving a vehicle using information extracted from multiple EEG signals, reducing the original dataset by more than 50% and maximizing the model's predictive capacity, achieving a precision rate greater than 90%.
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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.