An Investigation to Detect Driver Drowsiness from Eye blink Artifacts Using Deep Learning Models
Driver drowsiness is a well known problem that depreciates road safety that could cause road accidents, worldwide. Researchers are increasingly using the eye/eyelid images or the electroencephalogram's (EEG) spectral information to detect drowsiness in drivers. However, no attempt has been made...
Saved in:
Main Authors: | Egambaram, A., Badruddin, N. |
---|---|
Format: | Conference or Workshop Item |
Published: |
Institute of Electrical and Electronics Engineers Inc.
2022
|
Online Access: | http://scholars.utp.edu.my/id/eprint/37626/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85152437223&doi=10.1109%2fIECBES54088.2022.10079592&partnerID=40&md5=2df3b78840bb6741cbdbf67d0a1e6d65 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
ONLINE DETECTION AND REMOVAL OF EYE BLINK ARTIFACTS FROM
ELECTROENCEPHALOGRAM
by: EGAMBARAM, ASHVAANY
Published: (2020) -
Unsupervised Eye Blink Artifact Identification in Electroencephalogram
by: Egambaram, A., et al.
Published: (2019) -
Unsupervised Eye Blink Artifact Identification in Electroencephalogram
by: Egambaram, A., et al.
Published: (2019) -
Comparison of blind source separation methods for removal of eye blink artifacts from EEG
by: Soomro, M.H., et al.
Published: (2014) -
Comparison of blind source separation methods for removal of eye blink artifacts from EEG
by: Soomro, M.H., et al.
Published: (2014)