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dc.contributor.authorAli, Mirna
dc.contributor.authorAlsaedi, Nouf
dc.contributor.authorMian Qaisar, Saeed
dc.date.accessioned2023-04-29T12:13:52Z
dc.date.available2023-04-29T12:13:52Z
dc.date.issued2023-04-11
dc.identifier.doihttps://doi.org/10.1109/LT58159.2023.10092357en_US
dc.identifier.urihttp://hdl.handle.net/20.500.14131/742
dc.description.abstractPeople with disabilities struggle to perform specific tasks throughout their daily life. However, BCI systems are developed to assist people struggling with motor impairment by transforming their thoughts into action. Non-invasive BCI systems use electroencephalogram (EEG) to record brain activities. In this study, we segment the EEG signals and then break the segment down into a few intrinsic mode functions using oscillation mode decomposition. Then the intrinsic mode functions are mined for feature extraction. The features mined are processed by different machine learning algorithms for categorization. Among the different algorithms, K-NN yielded the best results with an overall average accuracy score of 95.48%. This approach can be used in future to develop the brain driven metaverse interactive solutions.en_US
dc.description.sponsorshipEffat Universityen_US
dc.publisherIEEEen_US
dc.subjectBrain Computer Interfaceen_US
dc.subjectMachine learning algorithmsen_US
dc.subjectFeature extractionen_US
dc.subjectClassificationen_US
dc.titleNon-Invasive BCI by using EMD and Machine Learning: A Metaverse Interaction Perspectiveen_US
dc.contributor.researcherDepartment Collaborationen_US
dc.subject.KSAHEALTHen_US
dc.contributor.ugstudent2en_US
dc.source.indexScopusen_US
dc.contributor.departmentElectrical and Computer Engineeringen_US
dc.contributor.firstauthorAli, Mirna
dc.conference.name2023 20th Learning and Technology Conference (L&T)en_US


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