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.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.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.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 |