English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Purpose: Human-machine collaboration is a promising strategy to improvehazard inspection. However, research on the effective integration of opinionsfrom humans with machines for optimal group decision making is lacking. Hence,considering the benefits of a brain-computer interface (BCI) to enableintuitive commutation, this study proposes a novel method to predict humanhazard response choices and decision confidence from brain activities for asuperior confidence-weighted voting strategy. Methodology: First, we developeda Bayesian inference-based algorithm to ascertain the decision threshold abovewhich a hazard is reported from human brain signals. This method was testedempirically with electroencephalogram (EEG) data collected in a laboratorysetting and cross-validated using behavioral indices of the signal detectiontheory. Subsequently, based on numerical simulations, the decision criteria forlow-, medium-, and high-confidence level differentiations characterized byparietal alpha-band EEG power were determined. Findings : The investigatedhazard recognition task was described as a process of probabilistic inferenceinvolving a decision uncertainty evaluation. The results demonstrated thefeasibility of EEG measurements in observing human internal representations ofhazard discrimination. Moreover, the optimal criteria to differentiate betweenlow-, medium-, and high-confidence levels were obtained by benchmarking againstan optimal Bayesian observer. Originality: This research demonstrates thepotential of a BCI as an effective channel for telecommunication, laying thefoundation for the design of future hazard detection techniques in thecollaborative human-machine systems research field.

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