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; https://doi.org/10.3390/s20205881
Received: 21 September 2020 / Revised: 9 October 2020 / Accepted: 13 October 2020 / Published: 17 October 2020
(This article belongs to the Special Issue Modern Trends and Applications of Intelligent Methods in Biomedical Signal and Image Processing)
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%.