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

TL;DR

This paper presents a distributed inference system that improves detection of task-specific ERPs in satellite image streams by using multiple optimized models, outperforming traditional single-model approaches.

Contribution

The novel system employs multiple specialized models for ERP detection in satellite images, enhancing accuracy and robustness over conventional methods.

Findings

Outperforms traditional single-model methods in ERP detection.

Including bounding boxes significantly improves recognition accuracy.

Achieves higher $F_{eta}$ scores across paradigms.

Abstract

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.