A STUDY OF DIFFERENT TYPES OF BRAINWAVES AND THEIR USE IN CONTROLLING REMOTE OBJECTS

dc.AffiliationOctober university for modern sciences and Arts MSA
dc.contributor.authorHASSANEIN, AHMED M. D. E
dc.contributor.authorIBRAHEM, SHROUK T. M
dc.contributor.authorHASSAN, NAYERA H. A
dc.date.accessioned2022-11-28T08:12:31Z
dc.date.available2022-11-28T08:12:31Z
dc.date.issued2022-11
dc.descriptionScopusen_US
dc.description.abstractIn this paper, we discussed the use of brainwaves to make an object move in one of two directions namely right or left. Our aim was to find the type of brain waves that was most suitable to interpret the intention of a volunteer when using his/her vision, hearing and pure thinking. We proposed a methodology in which the brain was excited using six stimulants which were Vision Right, Vision Left, Voice Right, Voice Left, Thinking Right and Thinking Left. Recordings of four volunteers were obtained using a Neurosky headset. Each recording contained 60 readings for one of eight types of brainwaves and a class representing the direction of motion intended by the volunteer. The average of the recordings for the right class was higher than that for the left one. Moreover, the standard deviation of the recordings for the left class was higher than that for the right. The average and standard deviation parameters meant that the identification of the recordings for the left class was higher than that for the right class. Moreover, the K-nearest neighbour was used to classify each recording to either right or left class. The Alpha family of brain waves showed values of relative standard deviation in the range of 80 to 120% for 5 stimulants out of the 6 stimulants tested. This meant that recordings from the Alpha family contained enough information to classify them correctly and identify the type of direction intended by the volunteer. In addition, they were not highly contaminated with noise. The Alpha2 brain wave type showed the highest accuracy in our classification problem. 82% was the percentage achieved for the Precision, Recall and F- measure parameters used to describe the accuracy of success in classification.en_US
dc.description.urihttps://www.scimagojr.com/journalsearch.php?q=21100806003&tip=sid&clean=0
dc.identifier.urihttp://repository.msa.edu.eg/xmlui/handle/123456789/5262
dc.language.isoen_USen_US
dc.publisherElsevier BVen_US
dc.relation.ispartofseriesJournal of Engineering Science and Technology;Vol. 17, No. 5 (2022) 3706 - 3725
dc.subjectBCI.en_US
dc.subjectBrainwavesen_US
dc.subjectClassificationen_US
dc.subjectEEGen_US
dc.subjectK-Nearest Neighboren_US
dc.subjectNeurosky headseten_US
dc.titleA STUDY OF DIFFERENT TYPES OF BRAINWAVES AND THEIR USE IN CONTROLLING REMOTE OBJECTSen_US
dc.typeArticleen_US

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