TY - JOUR
AU - 
AB - electronics Article Driving Drowsiness Detection with EEG Using a Modified Hierarchical Extreme Learning Machine Algorithm with Particle Swarm Optimization: A Pilot Study 1 , 2 3 3 1 4 4 Yuliang Ma , Songjie Zhang , Donglian Qi , Zhizeng Luo , Rihui Li , Thomas Potter 4 , and Yingchun Zhang * Institute of Intelligent Control and Robotics, Hangzhou Dianzi University, Hangzhou 310018, China; mayuliang@hdu.edu.cn (Y.M.); luo@hdu.edu.cn (Z.L.) Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou Dianzi University, Hangzhou 310018, China College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China; zsj1993@zju.edu.cn (S.Z.); qidl@zju.edu.cn (D.Q.) Department of Biomedical Engineering, University of Houston, Houston, TX 77204, USA; rli16@uh.edu (R.L.); tbpotter@uh.edu (T.P.) * Correspondence: yzhang94@uh.edu Received: 25 March 2020; Accepted: 28 April 2020; Published: 8 May 2020 Abstract: Driving fatigue accounts for a large number of trac accidents in modern life nowadays. It is therefore of great importance to reduce this risky factor by detecting the driver ’s drowsiness condition. This study aimed to detect drivers’ drowsiness using an advanced electroencephalography (EEG)-based classification technique. We first collected EEG data from six healthy adults under two di erent awareness conditions (wakefulness and drowsiness) in a virtual driving experiment. Five 
TI - Driving Drowsiness Detection with EEG Using a Modified Hierarchical Extreme Learning Machine Algorithm with Particle Swarm Optimization: A Pilot Study
JF - Electronics
DO - 10.3390/electronics9050775
DA - 2020-05-08
UR - https://www.deepdyve.com/lp/unpaywall/driving-drowsiness-detection-with-eeg-using-a-modified-hierarchical-sxj75lvl6h
DP - DeepDyve
ER -