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    This study introduces a novel phase-approaching method for brain-computer interfaces (BCI) using head-mounted devices. This approach enhances the number of available commands for steady-state visual evoked potential (SSVEP)-based BCI systems.

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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Human-Computer Interaction

    Background:

    • Steady-state visual evoked potential (SSVEP) offers high information transfer rate (ITR) and accuracy for brain-computer interfaces (BCI).
    • Head-mounted devices (HMDs) are increasingly used for SSVEP-based BCIs, but their fixed frame rates limit available stimulation frequencies.
    • This limitation restricts the number of commands achievable in HMD-based SSVEP BCIs.

    Purpose of the Study:

    • To propose a novel phase-approaching (PA) method for generating visual stimulation sequences at user-specified frequencies on HMDs.
    • To overcome the frequency limitations imposed by the fixed frame rates of HMDs.
    • To increase the number of available commands for SSVEP-based BCIs.

    Main Methods:

    • A phase-approaching (PA) method was developed to generate visual stimulation sequences (PAS sequences) approximating user-specified frequencies.
    • The PA method minimizes the phase difference between the generated sequence and an ideal wave at the target frequency.
    • Steady-state visual evoked potentials (SSVEPs) evoked by PAS sequences were analyzed using canonical correlation analysis (CCA) to identify the user's gazed target.

    Main Results:

    • A six-command SSVEP-based BCI was successfully implemented using the PA method to control a flying drone.
    • The system achieved a high information transfer rate (ITR) of 36.84 bits/min.
    • A high detection accuracy of 93.30% was recorded for identifying user commands.

    Conclusions:

    • The proposed phase-approaching (PA) method effectively increases the number of available commands for SSVEP-based BCIs on head-mounted devices (HMDs).
    • This method overcomes the frequency limitations inherent in HMDs with fixed frame rates.
    • The demonstrated performance indicates the potential of this PA method for practical BCI applications, such as controlling devices like flying drones.