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Updated: Sep 7, 2026

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
Published on: March 10, 2011
A study on Chinese college students' acceptance of brain-computer interface technology and its underlying logic-an
Jing Gao1, Jianfeng Hu1, Yang Chai1
1Yantai Institute of Science and Technology, Yantai, China.
Objective:
To examine the acceptance and influencing factors of brain-computer interface (BCI) technology among Chinese college students based on the extended Unified Theory of Acceptance and Use of Technology (UTAUT) model.
Methods:
A questionnaire survey was administered to 800 students recruited from 10 universities across eastern, central, and western China using a convergent mixed-methods design. Additionally, 40 students were selected for semi-structured in-depth interviews. We analyzed the quantitative data using partial least squares structural equation modeling (PLS-SEM) and the qualitative data using thematic analysis.
Results:
Teacher support (β = 0.337, p < 0.001), personal innovativeness (β = 0.219, p < 0.001), and effort expectancy (β = 0.156, p < 0.001) were positively associated with behavioral intention, while social influence showed a smaller, marginally significant association (β = 0.121, p = 0.049). Neither performance expectancy nor facilitating conditions turned out to be significant predictors, and neuroethical concerns did not reach significance either (β = -0.044). The model still accounted for a substantial 62.6% of the variance in behavioral intention (R2 = 0.626). On the qualitative side, four broad barrier categories emerged from the data: privacy anxiety, technological unfamiliarity, insufficient institutional readiness, and the absence of ethical review.
Conclusion:
The findings reveal that BCI technology acceptance follows a distinct psychological mechanism from traditional IT. Teacher support, rather than performance expectancy, emerges as the strongest predictor. Four main barriers were identified: privacy anxiety, technological unfamiliarity, insufficient institutional readiness, and the absence of ethical review. The study expands the UTAUT framework for neurotechnology applications in higher education and provides evidence-based recommendations for implementation.
