EL HADIRI, Abdejalil (Hassan II University ENSET Mohammedia), BAHATTI, Lhoussain (ENSET MOHAMMEDIA - HASSAN II university in Casablanca), El Magri, Abdelmounime (IESI Laboratory, Department of Electrical Engineering, ENSET Moh), Lajouad, Rachid (Hassan II university of Casablanca (Morocco), ENSET Mohammedia ()

A Novel Brain-Machine Safety System Based on Drowsiness Detection using the PNN and MLP Algorithms

Scheduled for presentation during the Regular Session "Miscellaneous" (FriS2T4), Friday, July 12, 2024, 12:10−12:30, Session room 4

12th IFAC Symposium on Control of Power & Energy Systems, July 10-12, 2024, Rabat, Morocco

This information is tentative and subject to change. Compiled on January 2, 2025

Abstract

This work aims to develop a safety system capable of detecting drowsiness, in order to reduce the accident rate, the main idea is based on the classification of the cerebral signal captured by the electroencephalograph, after processing to eliminate all noise, the resulting data will then be segmented into two classes, the first category is devoted to the state of wakefulness and the second will be devoted to drowsiness. This categorization is based on the combination of features such as mean and standard deviation, and performed by two neural network algorithms, the probabilistic neural network and the multilayer perceptron, whose performance is evaluated according to machine learning metrics such as accuracy, error, specificity, sensitivity, F1 indicator and ROC curve representation. The binary classification results are highly significant in terms of class discrimination, with both models achieving excellent performance, with the MLP classifier outperforming the PNN model with an accuracy of 96.10% and an accuracy rate of 98.43%. This suggests that the MLP model can be effectively applied to critical tasks where good discrimination is required, such as detecting drowsiness in drivers to avoid accidents.