BCI-Based Alcohol Patient Detection

dc.contributor.author	Vinothraj, Thangarajah
dc.contributor.author	Alfred, Denshiya Dominic
dc.contributor.author	Amarakeerthi, Senaka
dc.contributor.author	Ekanayake, Jayalath B.
dc.date.accessioned	2018-11-07T05:26:12Z
dc.date.available	2018-11-07T05:26:12Z
dc.date.issued	2017
dc.identifier.citation	Vinothraj, Thangarajah, Alfred,Denshiya Dominic, Amarakeerthi,Senaka, Ekanayake,Jayalath B., (2017), "BCI-Based Alcohol Patient Detection", IEEE	en_US
dc.identifier.isbn	978-1-5090-4917-2
dc.identifier.uri	http://dr.lib.sjp.ac.lk/handle/123456789/7051
dc.description.abstract	Attached	en_US
dc.description.abstract	This paper reviews the classification of Electroencephalogram (EEG) signals correlated with alcoholic and nonalcoholic subjects. EEG signals, which record the electrical activity in the brain, are useful for assessing the current mental status of a person. Alcohol consumption of people became a social problem as well as health hazards. Nowadays, more and more people wanted to travel back and forth to various places, With increasing of vehicular population and their movements on the roads, accidents are steadily increasing. Many road accidents are reported due to the consumption of alcohol by drivers and driving vehicles. This study investigates about the difference between drunked and non-drunked peoples brain signal using Electroencephalogram (EEG). EEG data is used for 20 alcoholic and 20 non-alcoholic subjects. Support Vector Machines were used for classifying EEG signals.
dc.language.iso	en	en_US
dc.publisher	IEEE	en_US
dc.subject	Electroencephalogram, Alcohol Detection, Electrodes, Brain Computer Interfaces	en_US
dc.title	BCI-Based Alcohol Patient Detection	en_US
dc.type	Article	en_US