Drowsiness analysis using common spatial pattern and extreme learning machine based on electroencephalogram signal

• Main Authors: Osmalina Nur Rahma, Akif Rahmatillah
• Format: Article
• Language: English
• Published: Wolters Kluwer Medknow Publications 2019-01-01
• Series: Journal of Medical Signals and Sensors
• Subjects: Common spatial pattern; drowsiness; electroencephalogram; extreme learning machine
• Online Access: http://www.jmssjournal.net/article.asp?issn=2228-7477;year=2019;volume=9;issue=2;spage=130;epage=136;aulast=Rahma

An alarm system has become essential to prevent someone from drowsiness while driving, considering the high incidence due to fatigue or drowsiness. This study offered an alternative to overcome all the limitations provided by the conventional system to detect sleepiness based on the driver's brain electrical activity using wearable electroencephalogram (EEG), which is lighter and easy to use. The EEG signals were collected using EMOTIV Epoc + and then were decomposed into narrowband frequency, such as delta, theta, alpha, and beta using DWT. The relative power, as the result of feature extraction, then were processed further by calculating its variance using the common spatial pattern (CSP) method to optimize the accuracy of extreme learning machine (ELM). Comparison of relative power between awake and drowsy state showed that during the drowsy state, theta-wave, alpha-wave, and beta-wave were tend to be higher than in the awake state. However, despite with the help of ELM, the accuracy was not too high (below 87%). The feature extraction which continued by calculating its variance using CSP algorithm before classified by ELM obtained a high accuracy, even with small amount of data training. This showed that CSP combining with ELM could be useful to shorten the time in training/calibration session, yet still, obtained high accuracy in classifying the awake state and drowsy state. The overall average accuracy of testing ranged from 91.67% to 93.75%. This study could increase the ability of EEG in detecting drowsiness that is important to prevent the risk caused by driving in a drowsy state.