{"title":"无服务器脑电图数据检索与预处理框架","authors":"Bathsheba Farrow, S. Jayarathna","doi":"10.1109/IRI58017.2023.00045","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":290818,"journal":{"name":"2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Serverless Electroencephalogram Data Retrieval and Preprocessing Framework\",\"authors\":\"Bathsheba Farrow, S. Jayarathna\",\"doi\":\"10.1109/IRI58017.2023.00045\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":290818,\"journal\":{\"name\":\"2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI)\",\"volume\":\"35 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IRI58017.2023.00045\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IRI58017.2023.00045","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Serverless Electroencephalogram Data Retrieval and Preprocessing Framework
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.