Random Subspace K-NN Based Ensemble Classifier for Driver Fatigue Detection Utilizing Selected EEG Channels-Reference-Cited by-同舟云学术

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Random Subspace K-NN Based Ensemble Classifier for Driver Fatigue Detection Utilizing Selected EEG Channels

Published:2021-10-31 Issue:5 Volume:38 Page:1259-1270
ISSN:0765-0019
Container-title:Traitement du Signal
language:
Short-container-title:TS

Author:

Rashid Mamunur,Mustafa Mahfuzah,Sulaiman Norizam,Abdullah Nor Rul Hasma,Samad Rosdiyana

Funder

Fundamental Research Grant Scheme (FRGS)

PGRS

Publisher

International Information and Engineering Technology Association

Subject

Electrical and Electronic Engineering

Cited by 12 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Application and Challenges of EEG Signals in Fatigue Driving Detection;PROG BIOCHEM BIOPHYS;2024

2. Learning Ground Displacement Signals Directly from InSAR-Wrapped Interferograms;Sensors;2024-04-20

3. Detection of cardiac abnormalities from 12-lead ecg using complex wavelet sub-band features;Biomedical Physics & Engineering Express;2024-04-04

4. Cross Subject Myocardial Infarction Detection From Vectorcardiogram Signals Using Binary Harry Hawks Feature Selection and Ensemble Classifiers;IEEE Access;2024

5. EEG and EMG-Based Multimodal Driver Drowsiness Detection: A CWT and Improved VGG-16 Pipeline;Lecture Notes in Mechanical Engineering;2024

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