ROBUST SUBJECT-INDEPENDENT BCIS USING ATTENTION MECHANISM BASED DEEP LEARNING MODELS
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Date
2023
Authors
Keutayeva, Aigerim
Journal Title
Journal ISSN
Volume Title
Publisher
School of Engineering and Digital Sciences
Abstract
Brain-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.
Description
Keywords
Type of access: Restricted, Deep Learning models, Robust Subject-Independent BCIs
Citation
Keutayeva, A. (2023). Robust Subject-Independent BCIs using Attention Mechanism based Deep Learning models. School of Engineering and Digital Sciences