A Distributed Inference System for Detecting Task-wise Single Trial Event-Related Potential in Stream of Satellite Images
This work addresses the challenge of improving ERP detection for brain-computer interface applications in satellite imagery, representing an incremental advancement over traditional single-model methods.
The paper tackles the problem of detecting task-wise single-trial event-related potentials in satellite image streams by introducing a Distributed Inference System that uses multiple models optimized for specific tasks, achieving the highest Fβ scores in experiments with four participants across two paradigms.
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 $F_β$ 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.