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    Area of Science:

    • Neuroscience
    • Human-Computer Interaction
    • Biomedical Engineering

    Background:

    • Steady-State Visual Evoked Potentials (SSVEP) Brain-Computer Interfaces (BCI) offer high information transmission rates (ITR) but lack portability and natural environmental interaction.
    • Augmented Reality (AR) integration is a promising approach to overcome these limitations in BCI systems.

    Purpose of the Study:

    • To develop and evaluate a portable and natural AR-BCI system using Microsoft HoloLens.
    • To investigate the impact of different signal processing algorithms on AR-BCI system performance.

    Main Methods:

    • Combined AR technology with SSVEP-BCI using Microsoft HoloLens.
    • Collected Electroencephalography (EEG) data in an AR environment.
    • Compared the performance of extended filter bank canonical correlation analysis (eFBCCA) against task-related component analysis (TRCA).

    Main Results:

    • The extended filter bank canonical correlation analysis algorithm outperformed task-related component analysis for SSVEP signal processing in the AR environment.
    • Achieved average recognition accuracies of 87.7%, 95.4%, and 97.6% for EEG data lengths of 1s, 1.5s, and 2s, respectively.
    • Obtained information transmission rates (ITR) of 64.6 bit/min, 62.9 bit/min, and 55.6 bit/min for EEG data lengths of 1s, 1.5s, and 2s, respectively, showing significant improvement over existing AR-BCI studies.

    Conclusions:

    • The developed AR-BCI system offers a more portable and natural BCI solution.
    • The extended filter bank canonical correlation analysis is a suitable algorithm for SSVEP-BCI in AR environments.
    • The integration of AR significantly enhances the performance and usability of SSVEP-BCI systems.