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    Ear electroencephalography (EEG) offers a more convenient brain-computer interface (BCI). This study enhances SSVEP BCI accuracy using ear-EEG by developing an error correction regression framework.

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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Signal Processing

    Background:

    • Ear-electroencephalography (EEG) offers a more unobtrusive and mobile alternative to traditional scalp-EEG for brain-computer interface (BCI) applications.
    • A key limitation of ear-EEG is its reduced ability to capture signals from non-temporal lobe areas, impacting paradigms like steady-state visual evoked potentials (SSVEPs) generated in the occipital region.
    • Maintaining high decoding accuracy for SSVEP BCI with ear-EEG is challenging due to signal attenuation and distortion.

    Purpose of the Study:

    • To improve the decoding accuracy of ear-EEG for SSVEP BCI by estimating occipital brain signals.
    • To develop and evaluate an ensemble method to account for prediction variability in regression models.
    • To introduce an error correction regression (ECR) framework, incorporating kernel ridge regression, to minimize prediction errors in ear-EEG based SSVEP BCI.

    Main Methods:

    • Investigated linear and nonlinear regression techniques to enhance ear-EEG decoding accuracy for SSVEPs.
    • Employed an ensemble approach to manage prediction variability from regression methods.
    • Proposed and validated an error correction regression (ECR) framework with an additional nonlinear regression step.

    Main Results:

    • The ECR framework demonstrated robust performance across single-session, session-to-session, and subject-transfer decoding scenarios.
    • Online decoding capabilities were validated using a short-time window.
    • Achieved average accuracies of 91.11±9.14% (single session), 90.52±8.67% (session-to-session), and 86.96±12.13% (subject-transfer).

    Conclusions:

    • The proposed ECR framework significantly enhances the reliability and accuracy of SSVEP BCI using ear-EEG.
    • Ear-EEG, when combined with advanced signal processing techniques like ECR, can achieve dependable performance for complex BCI applications.
    • This research paves the way for more practical and widespread adoption of ear-EEG in BCI technology.