A Distributed Inference System for Detecting Task-wise Single Trial
  Event-Related Potential in Stream of Satellite Images
Sung-Jin Kim Heon-Gyu Kwak Hyeon-Taek Han Dae-Hyeok Lee Ji-Hoon Jeong Seong-Whan Lee
Brain-computer interface (BCI) has garnered the significant attention for
their potential in various applications, with event-related potential (ERP)
performing a considerable role in BCI systems. This paper introduces a novel
Distributed Inference System tailored for detecting task-wise single-trial ERPs
in a stream of satellite images. Unlike traditional methodologies that employ a
single model for target detection, our system utilizes multiple models, each
optimized for specific tasks, ensuring enhanced performance across varying
image transition times and target onset times. Our experiments, conducted on
four participants, employed two paradigms: the Normal paradigm and an AI
paradigm with bounding boxes. Results indicate that our proposed system
outperforms the conventional methods in both paradigms, achieving the highest
$F_{\beta}$ scores. Furthermore, including bounding boxes in the AI paradigm
significantly improved target recognition. This study underscores the potential
of our Distributed Inference System in advancing the field of ERP detection in
satellite image streams.