An Innovative Method for Identifying Driving Weariness in EEG Data Using a Modified Machine Learning Algorithm

This paper thoroughly examines current methodologies and algorithms used to analyze electroencephalography (EEG) data to detect fatigue driving states. First, traditional procedures are examined, uncovering their time-consuming nature and elevated rates of mistakes. Different methods include collecting and preparing EEG data from the forehead. This paper examines traditional methods that include prolonged operation durations and the use of various frequency or time domain methods that apply non-machine learning algorithms. A new model is proposed in this paper, using machine learning methods with specific classifiers, especially K-Nearest Neighbors (KNN) and Random Forest (RF). The proposed model has excellent precision, as both classifiers attain impressive accuracy. The research also evaluates the model’s performance by using ROC curves, various performance matrices, and k-fold validation techniques for binary classification, demonstrating encouraging results. Future research will focus on using deep learning techniques, namely leveraging Deep Neural Networks, to improve the processing of EEG signals.