EEG based Drowsiness Detection using Relative Band Power and Short-time Fourier Transform

Pranesh Krishnan; Sazali Yaacob; Annapoorni Pranesh Krishnan; Mohamed Rizon; Chun Kit Ang

doi:10.2991/jrnal.k.200909.001

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Volume 7, Issue 3, December 2020, Pages 147 - 151

EEG based Drowsiness Detection using Relative Band Power and Short-time Fourier Transform

Authors

Pranesh Krishnan¹, Sazali Yaacob¹, Annapoorni Pranesh Krishnan¹, Mohamed Rizon²^{, *}, Chun Kit Ang²

¹Intelligent Automotive Systems Research Cluster, Electrical Electronic and Automation Section, Malaysian Spanish Institute Universiti Kuala Lumpur, Kulim, Kedah 09000, Malaysia

²Faculty of Engineering, Technology and Build Environment, UCSI University, No 1, Jalan Menara Gading, Taman Connaught, Kuala Lumpur 56000, Malaysia

^*Corresponding author. Email: MohdRizon@ucsiuniversity.edu.my

Corresponding Author

Mohamed Rizon

Received 6 November 2019, Accepted 16 April 2020, Available Online 11 September 2020.

DOI: 10.2991/jrnal.k.200909.001 How to use a DOI?
Keywords: Drowsiness; polysomnography; band power; short-time Fourier transform
Abstract: Sleeping on the wheels due to drowsiness is one of the major causes of death tolls all over the world. The objective of this research article is to classify drowsiness with alertness based on the Electroencephalogram (EEG) signals using spectral and band power features. A publicly available ULg DROZY database used in this research. Algorithms are developed to extract the five EEG channels from the raw multimodal signal. By using a higher-order Butterworth low pass filter, the high-frequency components above 50 Hz are removed. Another bandpass filter bank separates the raw signals into eight sub-bands, namely delta, theta, low alpha, high alpha, low beta, mid beta, high beta and gamma. During pre-processing step, the signals are segmented into an equal number of frames. An overlap of 50% and a frame duration of 2 s using a rectangular time windowing approach segments the signal into frames. Then, the feature extraction algorithm extracts the relative band power features based on the short-time Fourier transform for each frame. The extracted feature sets are further normalized and labelled as drowsy and alert and then combined to form the final dataset. K-fold cross-validation method is used. The dataset is trained using K-Nearest Neighbor algorithm (KNN) and support vector machine classifiers, and the results are compared. The KNN classifier produces 96.1% (dataset 1) and 95.5% (dataset 2) classification accuracy.
Open Access: This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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Next Article In Issue>

Journal: Journal of Robotics, Networking and Artificial Life
Volume-Issue: 7 - 3
Pages: 147 - 151
Publication Date: 2020/09/11
ISSN (Online): 2352-6386
ISSN (Print): 2405-9021
DOI: 10.2991/jrnal.k.200909.001 How to use a DOI?
Open Access: This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

ris enw bib

TY  - JOUR
AU  - Pranesh Krishnan
AU  - Sazali Yaacob
AU  - Annapoorni Pranesh Krishnan
AU  - Mohamed Rizon
AU  - Chun Kit Ang
PY  - 2020
DA  - 2020/09/11
TI  - EEG based Drowsiness Detection using Relative Band Power and Short-time Fourier Transform
JO  - Journal of Robotics, Networking and Artificial Life
SP  - 147
EP  - 151
VL  - 7
IS  - 3
SN  - 2352-6386
UR  - https://doi.org/10.2991/jrnal.k.200909.001
DO  - 10.2991/jrnal.k.200909.001
ID  - Krishnan2020
ER  -

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