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EEG-Based User Reaction Time Estimation Using Riemannian Geometry Features.

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    Summary

    This study introduces Riemannian tangent space features for electroencephalogram (EEG) brain-computer interface (BCI) regression. This novel approach improves reaction time estimation accuracy in vigilance tasks compared to traditional methods.

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    Area of Science:

    • Neuroscience
    • Machine Learning
    • Signal Processing

    Background:

    • Riemannian geometry has shown success in brain-computer interface (BCI) classification.
    • BCI regression problems are an important application area for BCIs.
    • Traditional feature extraction methods for electroencephalogram (EEG) signals may not be optimal for regression tasks.

    Purpose of the Study:

    • To apply Riemannian geometry to BCI regression problems for the first time.
    • To propose a novel feature extraction method for EEG-based BCI regression.
    • To evaluate the performance of the proposed method in reaction time estimation.

    Main Methods:

    • A spatial filter was employed to enhance EEG signal quality and reduce dimensionality.
    • Riemannian tangent space features were extracted from preprocessed EEG data.
    • The approach was validated on EEG signals from a sustained-attention psychomotor vigilance task.

    Main Results:

    • The proposed tangent space features significantly improved reaction time estimation.
    • Compared to powerband features, tangent space features reduced root mean square error by 4.30%-8.30%.
    • Estimation correlation coefficients increased by 6.59%-11.13% with tangent space features.

    Conclusions:

    • Riemannian tangent space feature extraction is effective for EEG-based BCI regression.
    • This method offers superior performance over traditional features for reaction time estimation.
    • The findings open new avenues for applying Riemannian geometry in BCI research.