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Brain-computer interface (BCI) has garnered the significant attention fortheir potential in various applications, with event-related potential (ERP)performing a considerable role in BCI systems. This paper introduces a novelDistributed Inference System tailored for detecting task-wise single-trial ERPsin a stream of satellite images. Unlike traditional methodologies that employ asingle model for target detection, our system utilizes multiple models, eachoptimized for specific tasks, ensuring enhanced performance across varyingimage transition times and target onset times. Our experiments, conducted onfour participants, employed two paradigms: the Normal paradigm and an AIparadigm with bounding boxes. Results indicate that our proposed systemoutperforms the conventional methods in both paradigms, achieving the highest$F_{\beta}$ scores. Furthermore, including bounding boxes in the AI paradigmsignificantly improved target recognition. This study underscores the potentialof our Distributed Inference System in advancing the field of ERP detection insatellite image streams.

A Distributed Inference System for Detecting Task-wise Single Trial Event-Related Potential in Stream of Satellite Images