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Denoising depth EEG signals during DBS using filtering and subspace decomposition.

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    This study introduces a new method to remove deep brain stimulation (DBS) artifacts from stereoelectroencephalography (SEEG) recordings. This technique helps recover crucial brain activity masked by stimulation, improving epilepsy diagnosis.

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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Signal Processing

    Background:

    • Stereoelectroencephalography (SEEG) is vital for exploring brain structures in epilepsy patients.
    • Deep brain stimulation (DBS) aids in defining the epileptogenic zone but contaminates SEEG signals.
    • Artifacts from DBS obscure local brain electrophysiological activity.

    Purpose of the Study:

    • To detrend and denoise SEEG signals contaminated by DBS.
    • To recover masked physiological brain activity for accurate epilepsy diagnosis.
    • To develop and validate an effective signal processing approach for DBS-SEEG data.

    Main Methods:

    • Review of existing filtering techniques for signal processing.
    • Proposal of a novel approach combining filtering with Generalized Eigenvalue Decomposition (GEVD).
    • Application of Singular Spectrum Analysis (SSA) combined with GEVD for enhanced artifact removal.

    Main Results:

    • The proposed GEVD-based filtering approach successfully separates DBS artifacts from brain activity.
    • Experimental validation using simulated and real SEEG data confirmed the method's efficacy.
    • The SSA-GEVD combination yielded the best performance in artifact separation and signal recovery.

    Conclusions:

    • The developed signal processing method effectively removes DBS artifacts from SEEG recordings.
    • This technique enhances the ability to recover masked physiological brain activity.
    • The findings contribute to more accurate epilepsy diagnosis and treatment planning.