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Electrical signatures characteristic of complex neurological activity and neuropsychiatric disease are embedded in electroencephalography (EEG) signal data. To firmly establish new correlations between these brain electrical pulses and cognition, behavior, and disorders, researchers must achieve adequate statistical power to validate and mitigate uncertainties in their findings. This necessitates the usage of extensive studies involving large volumes of raw EEG data files from multiple subjects, data which must be preprocessed before conducting further analysis. While conventional processing and analysis of these raw data have been performed using isolated physical lab environments and stovepiped applications, there is a growing necessity for processing and analysis solutions that enable distributed processing of large data collections. This study presents a novel microservices approach as an alternative and complementary solution for retrieving and preprocessing EEG signal data. The approach leverages serverless technologies to deliver a highly scalable solution for processing massive amounts of EEG data. Deployed within a public cloud environment, we assess the efficacy of this method when employing various container orchestration configurations. This work demonstrates the capability for substantial enhancements in processing speeds, particularly when dealing with extensive EEG datasets. 

Electroencephalogram (EEG) research continues to rely heavily on data silos used in isolated physical lab environments. However, as a part of the digital transformation, the EEG community has begun its exploration of the public cloud to determine how it can be best utilized to increase collaboration and accelerate research outcomes. The growing number of online repositories for data and tools has provided additional computational resources but the process of downloading data and software along with the installation and configuration requirements is cumbersome and prone to error. To break away from this research paradigm, we present a novel application of cloud technologies to provide reusable EEG data acquisition and preprocessing software as a service (SaaS) that eliminates data and software downloading prerequisites. We utilize the Amazon Web Services (AWS) cloud platform and serverless technologies to create a distributed, highly scalable and extensible solution for EEG signal data preprocessing that is more conducive to effective collaboration and data reproducibility with the potential to expedite neurotechnology breakthroughs.