EEG based Drowsiness Prediction Using Machine Learning Approach

Abstract

Drowsiness is the main cause of road accidents and it leads to severe physical injury, death, and significant economic losses. To monitor driver drowsiness various methods like Behaviour measures, Vehicle measures, Physiological measures and Hybrid measures have been used in previous research. This paper mainly focuses on physiological methods to predict the driver’s drowsiness. Several physiological methods are used to predict drowsiness. Among those methods, Electroencephalography is one of the non-invasive physiological methods to measure the brain activity of the subject. EEG brain signal extracted from the human scalp is analysed with various features and used for various health application like predicting drowsiness, fatigue etc. The main objective of the proposed system is to early predict the driver drowsiness with high accuracy so that we have divided our work into two steps. The first step is to collect the publicly available dataset of EEG based Eye state as (Eye open and Eye closed) where the signal acquisition process was done from Emotiv EEG Neuroheadset (14 electrodes) and analysed various feature engineering techniques and statistical techniques. The second step was applied with the machine learning classification model as K-NN and performance-based predicting models are used. In the Existing System, they used various machine learning classification models like K-NN and SVM for EEG Eye state classification and produced results around 80% -97%. Compared to the Existing system our proposed method produced better classification models for predicting driver drowsiness using different Feature engineering process and classification models as K-NN produced 98% of accuracy.