Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Feature Selection Model based on EEG signals to Assess the Cognitive Workload in Drivers

Version 1 : Received: 21 September 2020 / Approved: 22 September 2020 / Online: 22 September 2020 (11:27:03 CEST)

A peer-reviewed article of this Preprint also exists.

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

Journal reference: Sensors 2020, 20, 5881
DOI: 10.3390/s20205881

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 to analyze cognitive workload where the electroencephalographic (EEG) signals are the most used due to its high precision. However, one of the main challenges in the EEG signals implementing is finding the appropriate information to identify cognitive states. Here we show 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 workload 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 workload of subjects while driving a vehicle using information extracted from multiple EEG signals, reducing the original dataset by more than 50%, maximizing the model's predictive capacity-achieving a precision rate greater than 90%.

Subject Areas

electroencephalographic; feature selection; machine learning; prediction model

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