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An Immersive P300 Brain-Computer Interface Based on 3D Morphological Stimuli and Self-Adaptive Bayesian Linear

Junhong Luo1,2, Mengnan Zhu1, Yongbo Xiao3

  • 1School of Artificial Intelligence, Guangzhou Maritime University, Guangzhou 510725, China.

Biomimetics (Basel, Switzerland)
|June 25, 2026
PubMed
Summary

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This summary is machine-generated.

This study introduces an immersive P300 brain-computer interface (BCI) using 3D-Morph stimulation and self-adaptive Bayesian linear discriminant analysis (SA-BLDA). The novel approach enhances accuracy and efficiency while reducing user workload compared to traditional 2D BCIs.

Area of Science:

  • Neuroscience
  • Computer Science
  • Human-Computer Interaction

Background:

  • Conventional P300-based brain-computer interfaces (BCIs) often use 2D visual flashing, leading to visual fatigue and reduced immersion.
  • Limitations in visual immersion and user fatigue hinder the long-term usability and performance of traditional BCIs.

Purpose of the Study:

  • To develop and evaluate an immersive P300-BCI framework using a novel 3D-Morph stimulation paradigm.
  • To integrate the 3D-Morph paradigm with self-adaptive Bayesian linear discriminant analysis (SA-BLDA) for improved accuracy and efficiency.
  • To assess the impact of the proposed framework on classification performance, interaction efficiency, and user workload.

Main Methods:

  • Implemented a 3D-Morph paradigm utilizing dynamic 2D-to-3D morphological transformations in a virtual reality environment.
Keywords:
P300adaptive stopping strategybrain–computer interface (BCI)three-dimensional (3D)virtual reality (VR)

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  • Employed SA-BLDA to adapt the number of stimulation rounds based on classification confidence.
  • Conducted experiments with 24 participants comparing the 3D-Morph paradigm against a conventional 2D paradigm.

    • Main Results:

    • The 3D-Morph paradigm with SA-BLDA significantly improved offline classification accuracy (94.17%) and information transfer rate (ITR, 25.50 bits/min) compared to the 2D paradigm (87.29%, 22.75 bits/min).
    • Online experiments showed higher accuracy (91.46%) and ITR (37.23 bits/min) with the 3D-Morph system, alongside reduced response time and computational processing time.
    • Subjective workload assessments (NASA-TLX) indicated significantly lower user workload across all dimensions with the proposed immersive BCI framework.

    Conclusions:

    • The integration of 3D-Morph stimulation and SA-BLDA offers a significant advancement in P300-BCI technology.
    • This immersive framework enhances classification performance, interaction efficiency, and user experience.
    • The proposed system presents a feasible and practical solution for advanced BCI applications.