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ItemThe feedback dynamics of brain-computer interfaces in a distributed processing environment( 2022-01-04)This paper describes a distributed paradigm for human brain-computer interfaces that can incorporate machine learning-directly stimulus feedback to the subject. Specifically, we use OpenBCI hardware and software to capture real-time EEG (Electroencephalography) waveforms from a subject on a host ''client" computer and stream them to another ''server" computer which could perform complex analyses on the waveforms prior to sending commands back to the OpenBCI interface directing alterations to the stimulus. In addition to describing the conceptual system framework, we present here the test results quantifying the closed-loop system latencies under various conditions. Quantifying latency in any feedback control loop (in this case, one that actually contains the human subject's brain) is vital since excess latency can destabilize a system.
ItemSemantic Framework for Practicing Data Science in Public Health Organizations during the Covid-19 Pandemics( 2022-01-04)This paper proposes a semantic framework based on software architectures for accommodating data science practices to the needs of Public Health Organizations (PHO), during the COVID-19 pandemics. The goal is to create an environment suitable for deploying data science on an ad-hoc basis, upon the selection of data generated by governments, non-government organizations, public databases and social media, but guided by PHO own needs and expertise. It is important to run predictions, through learning technologies, which may depend on circumstances and situations relevant for PHO in the particular moment and thus enable better decision making in the time of the pandemic. The proposed software architecture relies on its deployment within integrated development environments and plug-ins/APIs towards software tools, and libraries for (a) data gathering and preprocessing, (b) predictions with learning technologies (c) reasoning with semantic technologies and (d) including human intervention to aid in understanding the situation in which PHO questions may be answered. The illustration of the proposal uses the sentiment analysis of Twitter data relevant to COVID-19 and classification of tweets with machine learning