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dc.contributor.advisorSabuj, Saifur Rahman
dc.contributor.advisorHuda, A.S. Nazmul
dc.contributor.authorMonowara, Syeda Maliha
dc.contributor.authorShariar, Md Ahnaf
dc.contributor.authorIslam, Md. Shafayat Ul
dc.contributor.authorJawad, Muhammed Junaid Noor
dc.date.accessioned2021-06-10T05:29:33Z
dc.date.available2021-06-10T05:29:33Z
dc.date.copyright2020
dc.date.issued2020-12
dc.identifier.otherID: 16221043
dc.identifier.otherID: 16121078
dc.identifier.otherID: 15221001
dc.identifier.otherID: 14121044
dc.identifier.urihttp://hdl.handle.net/10361/14519
dc.descriptionThis thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2020.en_US
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (pages 83-94).
dc.description.abstractIndividuals suffering from severe paralysis encounter a multitude of issues that influence their quality of life. Paralysis can occur due to impairments of the central nervous system (CNS) causes by brain-stroke, accidents, neurodegenerative dysfunctions or other factors. A significant portion of our society endures the consequences that limit their physical functionalities such as movement, communication, task performances and so on. In recent decades, technology has made substantial assistive devices that can communicate the brainwave signals and interpret these into commands. The development of the brain-computer interface (BCI) depends on the electric impulses generated in the brain. Hence, this can consequently be implemented for improvement purposes, that can eventually help to overcome the aspects of functional disabilities. To resolve the obstacles associated with paralysis, this project of the brainwave-assistive system is based on the internet-of-things (IoT). The system will be further comprised of multiple sensors that continuously acquire the brainwave frequencies for implementation through the connected microcontroller. For this project, the Cyton biosensing boards along with the WiFi shield have been utilised to read the generated electric signals from the brain which have been differentiated as per the functionality requirements. The WiFi shield enables the accumulated data to be saved in the database henceforth can be accessed at any instance (in real-time basis) through the software application. We have observed feedback generation through a microcontroller, we have further transmitted the data utilizing LSL to Python for the control of computer application. Furthermore, we intend to develop a mobile application that will frequently update the data that would enable the user to visualize the brainwave signals on a dashboard. Further research is required for a better understanding of the system to implement for extensive purposes such as home enhanced mobility, appliance control, emergency alarm-system and so forth.en_US
dc.description.statementofresponsibilitySyeda Maliha Monowara
dc.description.statementofresponsibilityMd Ahnaf Shariar
dc.description.statementofresponsibilityMd. Shafayat Ul Islam
dc.description.statementofresponsibilityMuhammed Junaid Noor Jawad
dc.format.extent95 pages
dc.language.isoen_USen_US
dc.publisherBRAC Universityen_US
dc.rightsBrac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.
dc.subjectIoTen_US
dc.subjectInternet of Thingsen_US
dc.subjectBCIen_US
dc.subjectEEGen_US
dc.subjectFFTen_US
dc.subjectOpenBCIen_US
dc.subjectBainwaveen_US
dc.titleIoT based brain-wave assistive system for paralyzed individualsen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Electrical and Electronic Engineering, Brac University
dc.description.degreeB. Electrical and Electronic Engineering


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