Skip navigation
Run Run Shaw Library City University of Hong KongRun Run Shaw Library

Please use this identifier to cite or link to this item: http://dspace.cityu.edu.hk/handle/2031/8745
Full metadata record
DC FieldValueLanguage
dc.contributor.authorIp, Wai Keien_US
dc.date.accessioned2017-03-08T06:23:32Z
dc.date.accessioned2017-09-19T09:15:52Z
dc.date.accessioned2019-02-12T07:34:46Z-
dc.date.available2017-03-08T06:23:32Z
dc.date.available2017-09-19T09:15:52Z
dc.date.available2019-02-12T07:34:46Z-
dc.date.issued2016en_US
dc.identifier.other2016eeiwk306en_US
dc.identifier.urihttp://144.214.8.231/handle/2031/8745-
dc.description.abstractEnergy drink is one of the most common drinks for human to stay alert during work when they feel tired. Students especially university students usually drink energy drinks when deadlines of assignment or examinations approach. This project focuses on the effect of energy drink (RedBull Energy Drink) on Single-Channel EEG of the fontal area (Fp1) when people are taking different reasoning tests. The raw data of the frontal EEG is extracted from the MindWave Headset manufactured by NeuroSky. The signal was performed the band pass filter and the removal of artifacts before performing the analysis. The band powers of EEG is used as the features of the study, using statistical significant tests and machine learning methods such as k-nearest neighbors, support vector machine, sign test etc. The result show that people with the intake of energy drink containing caffeine, Taurine and high amount of sugar (sucrose and glucose) have a significant difference to people without the intake of the energy drink within 24 hours and can be classified by different machine learning classifiers.en_US
dc.rightsThis work is protected by copyright. Reproduction or distribution of the work in any format is prohibited without written permission of the copyright owner.en_US
dc.rightsAccess is restricted to CityU users.en_US
dc.titleEffect of energy drink on EEG in performing reasoning testsen_US
dc.contributor.departmentDepartment of Electronic Engineeringen_US
dc.description.supervisorSupervisor: Dr. Chan, Rosa H M; Assessor: Dr. Cheng, L Len_US
Appears in Collections:Electrical Engineering - Undergraduate Final Year Projects 

Files in This Item:
File SizeFormat 
fulltext.html146 BHTMLView/Open
Show simple item record


Items in Digital CityU Collections are protected by copyright, with all rights reserved, unless otherwise indicated.

Send feedback to Library Systems
Privacy Policy | Copyright | Disclaimer