Quantum Machine Learning for Drowsiness Detection with EEG Signals
Lavìnia Maria Mendes Araújo, Plínio Marcio da Silva Ramos, Isis Didier Lins +6 more
Human reliability is increasingly important in accident prevention, and monitoring biological parameters can help detect patterns indicating behaviors that may lead to accidents.Electroencephalogram (EEG) data has been used to identify drowsiness, a major cause of fatigue in machine operators in the oil and gas industry.While classic machine learning methods like Multilayer Perceptron (MLP) have been used with EEG data, quantum computing has shown promise in solving complex problems efficiently.Variational Quantum Algorithms are one example of quantum concepts applied to classical structures for data training.This study aims to classify operator drowsiness using Quantum Machine Learning (QML) models.EEG signals are preprocessed to extract relevant features such as Higuchi Fractal Dimension, Complexity, and Mobility, as well as statistical features.QML models are trained with various quantum circuit layers, rotation, and entanglement gates.Results will be compared with classical MLP models.This work contributes to exploring the context of drowsiness in QML, which has not been extensively studied in the literature.It serves as a proof of concept that QML models are suitable for this type of data and can be further improved as Quantum Computing continues to evolve.
European Safety and Reliability Conference (ESREL)•Jan 1, 2023•0 citations