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Spatial Filtering in SSVEP-Based BCIs: Unified Framework and New Improvements.

Chi Man Wong, Boyu Wang, Ze Wang

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    |February 25, 2020
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    This study unifies spatial filtering algorithms for steady-state visual evoked potential (SSVEP) brain-computer interfaces (BCIs). A new framework reveals algorithm relationships and enables the development of improved spatial filtering methods for enhanced performance.

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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Signal Processing

    Background:

    • Spatial filtering is crucial for enhancing signal-to-noise ratio (SNR) in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs).
    • Numerous spatial filtering algorithms exist, but their interconnections and differences remain underexplored, hindering further development.

    Purpose of the Study:

    • To propose a unified framework for understanding and categorizing existing spatial filtering algorithms in SSVEP-BCIs.
    • To develop novel spatial filtering algorithms with improved performance.

    Main Methods:

    • Formulating spatial filtering algorithms as generalized eigenvalue problems (GEPs).
    • Analyzing nineteen mainstream spatial filtering algorithms within the GEP framework.
    • Designing and validating new spatial filtering algorithms on public SSVEP datasets.

    Main Results:

    • Revealed similarities, differences, and relationships among nineteen spatial filtering algorithms.
    • Identified origins of algorithms from canonical correlation analysis (CCA), principal component analysis (PCA), and multi-set CCA.
    • Developed three new spatial filtering algorithms demonstrating enhanced performance on two datasets with 45 subjects.

    Conclusions:

    • The proposed GEP framework offers insights into the relationships between spatial filtering algorithms.
    • The framework facilitates the design of new, high-performance spatial filtering algorithms for SSVEP-BCIs.