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dc.contributor.advisorParvez, Mohammad Zavid
dc.contributor.authorAfroz, Sharmin
dc.contributor.authorShimanto, Zubaed Hassan
dc.contributor.authorJahan, Ra qua Sifat
dc.date.accessioned2019-07-14T05:00:16Z
dc.date.available2019-07-14T05:00:16Z
dc.date.copyright2019
dc.date.issued2019-04
dc.identifier.otherID 15101041
dc.identifier.otherID 14301057
dc.identifier.otherID 14101123
dc.identifier.urihttp://hdl.handle.net/10361/12352
dc.descriptionThis thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2019.en_US
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (pages 34-39).
dc.description.abstractCognitive load and emotional states may impact for designing an assistive navigation aid for the Visually Impaired Peoples (VIPs). In this study, electroencephalogram (EEG) signals were captured from participants with di erent degree sight loss peoples (DDSLPs). EEG signals were then used to measure various cognitive loads and emotions to test the usability of an intelligent navigation aids. To support the argument of testing the usability of a navigation aids, the complexity of the tasks in terms of cognitive load and emotions were quanti ed considering diverse factors by extracting features from various well established entropies when DDSLPs will navigate unfamiliar indoor environments with di erent obstacles. Experimental results show that classi cation accuracy for narrow space is 97.61% for cognitive load. Moreover, the experiment achieves that 90.40% and 50.60% classi cation accuracy for arousal and valence in the open space and stairs, respectively.en_US
dc.description.statementofresponsibilitySharmin Afroz
dc.description.statementofresponsibilityZubaed Hassan Shimanto
dc.description.statementofresponsibilityRa qua Sifat Jahan
dc.format.extent40 pages
dc.language.isoenen_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.subjectCognitive loaden_US
dc.subjectEmotional statesen_US
dc.subjectEEGen_US
dc.subjectHuman brainen_US
dc.subjectEntropyen_US
dc.subjectSVMen_US
dc.subject.lcshMachine learning
dc.titleExploring cognitive load and emotional states for the visually impaireden_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, Brac University
dc.description.degreeB. Computer Science and Engineering


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