Mobile EEG Based Drowsiness Detection using K-Nearest Neighbor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this research, a drowsiness detection system, named Drowsiver, was developed for a mobile electroencephalograph (EEG) and a mobile phone. The system is expected to minimize the causes of accidents caused by drowsy drivers. By using Electroencephalogram (EEG), the condition of drowsiness is detected by recording the electrical activity that occurs in the human brain and is represented as a frequency signal. The signal is transmitted to the Android mobile application via Bluetooth and will give an alarm notification if the drowsiness is detected. The brainwave from the mobile EEG is processed using Fast Fourier Transform (FFT) to extract its features. These features are classified using K-Nearest Neighbor (KNN) classifier. The system produces the best performance with the highest accuracy of 95.24% using the value of k=3 and four brain waves as features, namely Delta, Theta, Alpha, and Beta waves.

Original languageEnglish
Title of host publication2019 IEEE 10th International Conference on Awareness Science and Technology, iCAST 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728138213
DOIs
Publication statusPublished - Oct 2019
Event10th IEEE International Conference on Awareness Science and Technology, iCAST 2019 - Morioka, Japan
Duration: 23 Oct 201925 Oct 2019

Publication series

Name2019 IEEE 10th International Conference on Awareness Science and Technology, iCAST 2019 - Proceedings

Conference

Conference10th IEEE International Conference on Awareness Science and Technology, iCAST 2019
CountryJapan
CityMorioka
Period23/10/1925/10/19

Keywords

  • Android Application
  • Brain Wave
  • Drowsiness
  • Electroencephalogram (EEG)

Fingerprint Dive into the research topics of 'Mobile EEG Based Drowsiness Detection using K-Nearest Neighbor'. Together they form a unique fingerprint.

Cite this