Collaborative Brain-Computer Interface for Human Interest Detection in Complex and Dynamic Settings

TL;DR

This paper introduces a collaborative brain-computer interface system that passively detects group interest in complex, dynamic environments by merging neural signals across team members, using deep learning to adapt to real-world scenarios.

Contribution

It demonstrates the application of cBCI in naturalistic settings with virtual environment scanning and employs an experiment-agnostic deep learning model for real-world use cases.

Findings

Improved interest detection accuracy with more subjects in the ensemble

Potential to reconstruct target events in noisy, complex environments

Validates cBCI utility in dynamic, real-world scenarios

Abstract

Humans can fluidly adapt their interest in complex environments in ways that machines cannot. Here, we lay the groundwork for a real-world system that passively monitors and merges neural correlates of visual interest across team members via Collaborative Brain Computer Interface (cBCI). When group interest is detected and co-registered in time and space, it can be used to model the task relevance of items in a dynamic, natural environment. Previous work in cBCIs focuses on static stimuli, stimulus- or response- locked analyses, and often within-subject and experiment model training. The contributions of this work are twofold. First, we test the utility of cBCI on a scenario that more closely resembles natural conditions, where subjects visually scanned a video for target items in a virtual environment. Second, we use an experiment-agnostic deep learning model to account for the real-world use case where no training set exists that exactly matches the end-users task and circumstances. With our approach we show improved performance as the number of subjects in the cBCI ensemble grows, and the potential to reconstruct ground-truth target occurrence in an otherwise noisy and complex environment.