학술논문

Fatigue Driving Detection with Artificial Bee Colony Algorithm
Document Type
Conference
Source
2022 6th Asian Conference on Artificial Intelligence Technology (ACAIT) Artificial Intelligence Technology (ACAIT), 2022 6th Asian Conference on. :1-7 Dec, 2022
Subject
Computing and Processing
Robotics and Control Systems
Signal Processing and Analysis
Time-frequency analysis
Transforms
Fatigue
Feature extraction
Artificial bee colony algorithm
Electroencephalography
Iterative algorithms
fatigue driving detection
EEG
wavelet transform
feature selection
artificial bee colony algorithm
Language
Abstract
Fatigue driving detection (FDD) with electroencephalogram (EEG) is an effective manner for ensuring the driving safety. As EEG signals used for recognition contain a large amount of information, they are always time-consuming to be processed. In order to improve the efficiency and accuracy of FDD, 31 features are extracted from time domain, frequency domain, time-frequency domain respectively in this paper, and a dimension reduction strategy (FDR) is introduced in the initialization stage of the artificial bee colony (ABC) algorithm to reduce the number of features. The results show that the efficiency of the proposed algorithm is 32.5% higher than that of traditional one for mental fatigue detection.