Neuro-Trust and System Trust: A Dual-Track Trust Structure and Antecedent Patterns in EEG-BCI Attention Training

As brain–computer interfaces (BCIs) move toward training, rehabilitation, and consumer use, users must judge both whether a system is technically reliable and whether it credibly represents their own neural or attentional state. This study tests whether trust after an initial EEG attention-training session is better represented by two related yet distinguishable targets: Neuro-Trust (NT), trust in the system’s representation of one’s internal state, and System Trust (ST), trust in the overall device–algorithm–training system. Candidate antecedents were generated from interviews with 18 users and comments from five platforms; survey data from 300 young adults yielded 274 valid cases. A nine-factor confirmatory factor analysis fit better than an eight-factor model merging NT and ST (Δχ2(8) = 327.155, p < 0.001; HTMT = 0.411). Structural equation modeling showed that perceived feedback explainability (PFE) related positively to both NT and ST, whereas perceived attention-regulation value (PARV) related to NT only (Δχ2(1) = 21.655, p < 0.001; bootstrap Δβ = 0.388, 95% CI [0.168, 0.607]). Out-of-fold SHAP analysis under repeated nested cross-validation reproduced this pattern: PARV ranked first for NT but fifth for ST, whereas PFE ranked second and first. BCI trust may thus comprise related but distinguishable dimensions with shared and limited track-specific antecedents.