Utilización de la red neuronal para la detección de somnolencia basada en señales de EEG y optimización en la selección de sus características mediante algoritmo genético

  • Sepehr Sarabi Islamic Azad University
  • Milad Asadnejad Islamic Azad University
  • Saman Rajabi Seraj Higher Education Institute
Palabras clave: Drowsiness, EEG Signal, Optimization, Genetic Algorithm, Neural Network, Perceptron, Radial Base Functions


One of the major causes of traffic accidents is driver’s drowsiness. For this reason, detecting whether the driver's eyes are open or closed is one of the critical factors in reducing road deaths. One way to detect whether your eyes are open or closed is to use EEG signals. EEG signals are obtained from the recording of electrical activity in the human brain. The present study uses a neural network that is applied to the driver's EEG signals to detect whether the eye is open or closed. The data of the EEG signals used in this paper consist of 14 features that are based on a statistical population of 600 people. Various neural network algorithms have been implemented for clustering these data into two classes of open or closed eyes, which are described in this paper. Perceptron neural network and radial base neural network (RBF) are two types of networks used in this paper. Also, in order to improve the execution speed and reduce the occupied space of the microcontroller, the genetic algorithm method has been used to optimize the fitting function of Fisher’s discriminant rate, in which the optimized function provides better results in the less occupied time and space.

Biografía del autor/a

Sepehr Sarabi, Islamic Azad University

Department of Medical Engineering, Faculty of Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran

Milad Asadnejad, Islamic Azad University

Department of Medical Engineering, Faculty of Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran

Saman Rajabi, Seraj Higher Education Institute

Department of Medical Engineering, Faculty of Engineering, Seraj Higher Education Institute, Tabriz, Iran


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