Weighing votes in human-machine collaboration for hazard recognition: Inferring hazard perceptual threshold and decision confidence from electroencephalogram wavelets

TL;DR

This study introduces a novel brain-computer interface method that predicts human hazard response choices and confidence levels from EEG signals, enhancing collaborative hazard detection in human-machine systems.

Contribution

It develops a Bayesian inference algorithm to determine hazard decision thresholds from EEG data and characterizes confidence levels using EEG power, advancing hazard recognition techniques.

Findings

EEG signals can observe internal hazard discrimination representations.

Optimal criteria for confidence levels were established using Bayesian benchmarking.

The approach supports improved decision weighting in human-machine collaboration.

Abstract

Purpose: Human-machine collaboration is a promising strategy to improve hazard inspection. However, research on the effective integration of opinions from humans with machines for optimal group decision making is lacking. Hence, considering the benefits of a brain-computer interface (BCI) to enable intuitive commutation, this study proposes a novel method to predict human hazard response choices and decision confidence from brain activities for a superior confidence-weighted voting strategy. Methodology: First, we developed a Bayesian inference-based algorithm to ascertain the decision threshold above which a hazard is reported from human brain signals. This method was tested empirically with electroencephalogram (EEG) data collected in a laboratory setting and cross-validated using behavioral indices of the signal detection theory. Subsequently, based on numerical simulations, the decision criteria for low-, medium-, and high-confidence level differentiations characterized by parietal alpha-band EEG power were determined. Findings : The investigated hazard recognition task was described as a process of probabilistic inference involving a decision uncertainty evaluation. The results demonstrated the feasibility of EEG measurements in observing human internal representations of hazard discrimination. Moreover, the optimal criteria to differentiate between low-, medium-, and high-confidence levels were obtained by benchmarking against an optimal Bayesian observer. Originality: This research demonstrates the potential of a BCI as an effective channel for telecommunication, laying the foundation for the design of future hazard detection techniques in the collaborative human-machine systems research field.