Next Article in Journal
Power-Saving Design of Radio Frequency Identification Sensor Networks in Bus Seatbelt Monitoring Systems
Previous Article in Journal
DNAzyme Sensor for the Detection of Ca2+ Using Resistive Pulse Sensing
Previous Article in Special Issue
Towards to Optimal Wavelet Denoising Scheme—A Novel Spatial and Volumetric Mapping of Wavelet-Based Biomedical Data Smoothing
Open AccessArticle

Feature Selection Model based on EEG Signals for Assessing the Cognitive Workload in Drivers

Department of Computer Architecture, Polytechnic University of Catalonia, 08034 Catalonia, Spain
Telecommunications Department, University of Colima, 28040 Colima, Mexico
Author to whom correspondence should be addressed.
Sensors 2020, 20(20), 5881;
Received: 21 September 2020 / Revised: 9 October 2020 / Accepted: 13 October 2020 / Published: 17 October 2020
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%. View Full-Text
Keywords: electroencephalographic; feature selection; machine learning; prediction model electroencephalographic; feature selection; machine learning; prediction model
Show Figures

Figure 1

MDPI and ACS Style

Becerra-Sánchez, P.; Reyes-Munoz, A.; Guerrero-Ibañez, A. Feature Selection Model based on EEG Signals for Assessing the Cognitive Workload in Drivers. Sensors 2020, 20, 5881.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop