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    This study introduces a Nonlinear Transformation (NLT) using Extreme Learning Machine Autoencoder for Brain-Computer Interfaces (BCI). This method enhances cross-subject recognition, significantly reducing calibration time for new users.

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    Area of Science:

    • Neuroscience
    • Computer Science
    • Biomedical Engineering

    Background:

    • Steady-State Visually Evoked Potential (SSVEP) is key for high-speed Brain-Computer Interfaces (BCI).
    • Inter-subject variability in EEG signals complicates BCI calibration, requiring significant time for new users.
    • Existing linear transformation methods struggle to capture complex, nonlinear EEG data relationships.

    Purpose of the Study:

    • To develop a Nonlinear Transformation (NLT) method using Extreme Learning Machine Autoencoder (ELM-AE) for SSVEP-based BCIs.
    • To improve cross-subject recognition accuracy and reduce calibration time for target subjects.
    • To map trials from source subjects to templates of a target subject for enhanced BCI performance.

    Main Methods:

    • Implemented an Extreme Learning Machine Autoencoder (ELM-AE) for Nonlinear Transformation (NLT) of SSVEP trials.
    • Mapped trials from existing subjects (source subjects) to one or a few templates from a target subject.
    • Evaluated the NLT method against Linear Standard Transformation (LST) for cross-subject classification.

    Main Results:

    • The NLT method achieved an average recognition accuracy of 84.23% with one template.
    • NLT outperformed LST across all template sizes and all 35 subjects.
    • NLT demonstrated superior performance compared to LST, with an average accuracy of 84.23% versus 82.19% for LST with one template.

    Conclusions:

    • Nonlinear Transformation (NLT) using ELM-AE is a feasible approach for SSVEP-based BCIs.
    • The NLT method effectively enhances cross-subject recognition and reduces calibration time.
    • Utilizing one or a few templates with NLT enables rapid calibration for new BCI users.