ROBUST SUBJECT-INDEPENDENT BCIS USING ATTENTION MECHANISM BASED DEEP LEARNING MODELS

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dc.contributor.authorKeutayeva, Aigerim
dc.date.accessioned2023-05-29T09:32:32Z
dc.date.available2023-05-29T09:32:32Z
dc.date.issued2023
dc.description.abstractBrain-Computer Interfaces can revolutionize human-computer interaction by enabling users to engage with technology through cognitive processes. BCIs exhibit extensive prospective applications, comprising the reinstatement of mobility and communication skills in disabled individuals, augmentation of human performance across diverse domains, and provision of novel instruments for scientific exploration. However, one of the significant challenges in developing BCIs is ensuring that they work with different people, regardless of their differences in cognitive abilities, language backgrounds, ages, and physical conditions. This thesis investigates robust subject-independent BCIs using attention mechanismbased deep learning models. The ability to create subject-independent BCIs is crucial for their practical use, as it can reduce the time and cost associated with individual calibration for each user. Additionally, robust subject-independent BCIs can help to improve accessibility for people with severe illnesses, such as amyotrophic lateral sclerosis (ALS), locked-in syndrome, and other conditions that limit mobility and communication abilities. This study uses attention mechanism-based deep learning models to identify the most informative features that are common across all subjects while filtering out noise and irrelevant information. We use two different types of BCI datasets, one based on Event-Related Potentials (ERPs) and the other based on Motor Imagery (MI), to evaluate the performance of our chosen approach. The results show that the attention mechanism-based deep learning models can achieve high levels of accuracy and robustness across different subjects and have the potential to improve the usability of BCIs in various applications.en_US
dc.identifier.citationKeutayeva, A. (2023). Robust Subject-Independent BCIs using Attention Mechanism based Deep Learning models. School of Engineering and Digital Sciencesen_US
dc.identifier.urihttp://nur.nu.edu.kz/handle/123456789/7135
dc.language.isoenen_US
dc.publisherSchool of Engineering and Digital Sciencesen_US
dc.rightsAttribution-NonCommercial-ShareAlike 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/us/*
dc.subjectType of access: Restricteden_US
dc.subjectDeep Learning modelsen_US
dc.subjectRobust Subject-Independent BCIsen_US
dc.titleROBUST SUBJECT-INDEPENDENT BCIS USING ATTENTION MECHANISM BASED DEEP LEARNING MODELSen_US
dc.typeMaster's thesisen_US
workflow.import.sourcescience

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