Date of Award

Summer 2024

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Computer Science

Program/Concentration

Computer Science

Committee Director

Sampath Jayarathna

Committee Member

Vikas Ashok

Committee Member

Sarah Hosni

Committee Member

Soo-Yeon Ji

Abstract

The rich information content of the brain is embedded in electroencephalography (EEG) data that instantaneously measures its electrophysiological activity. Expansion of EEG data usage for health risk predictions and other applications requires reliable and consistent methods for extracting features from raw signals. However, the intricate nature of EEG signal data analysis is further complicated by the magnitude of variability in current research practices, including data preprocessing strategies. The heavy reliance on antiquated, stove-piped applications and pipelines also highlights a need for improved solutions that enable efficient, distributed preprocessing of large EEG data collections.

To address these challenges, we propose a common cloud-based EEG signal data preprocessing and feature extraction solution. This innovative system, incorporating a microservices architecture and several serverless cloud technologies, can potentially revolutionize the field of EEG data analysis. We advocate for direct programmatic access to internal and external cloud-based data repositories, a capability that enhances efficiency, ease of use, and resource sharing. We have implemented these capabilities as software-as-a-service deployed in the public cloud, creating a highly accessible and scalable capability for processing large amounts of EEG signal data.

Our methodology, when employing various configurations for the container orchestration service, demonstrates significant and tangible performance improvements in speed and scalability. We measure and examine web vitals to assess the application’s performance, interactivity, and visual stability. The results for our solution were consistent with industry standards for responsive, quality software implementations. Furthermore, we consider usability in our application design and conduct a usability study of the implemented system to quantify its impacts on user efficiency, effectiveness, and satisfaction.

Based on the System Usability Scale (SUS), NASA Task Load Index (NASA-TLX), and After Scenario Questionnaire (ASQ) scores collected in this study, we found the usability of our cloud-based EEG signal data preprocessing and feature extraction software implementation to be exceptionally high. This conclusion is drawn from assessment scales for the survey instruments, which indicate a superior user experience. The assessments presented herein serve as evidence of the performance enhancements provided by the system, particularly with optimizations to processing speed and user efficiency when working with extensive EEG datasets.

Rights

In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/ This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).

DOI

10.25777/4g6w-pf61

ISBN

9798384455509

ORCID

0000-0003-2504-3667

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