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    Summary
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    This study introduces a novel real-time pipeline for electroencephalographic (EEG) artifact removal, combining artifact subspace reconstruction (ASR) and online recursive independent component analysis (ORICA). The method effectively removes transient and continuous artifacts for improved EEG analysis.

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    Area of Science:

    • Neuroscience
    • Signal Processing
    • Biomedical Engineering

    Background:

    • Electroencephalographic (EEG) signals are crucial for brain research but are often contaminated by artifacts.
    • Artifacts, such as movement and eye movements, can obscure neural activity, especially in real-time applications.
    • Existing offline artifact removal methods are insufficient for near real-time EEG processing.

    Purpose of the Study:

    • To develop and validate an effective online pipeline for detecting and removing artifacts from EEG data.
    • To enable real-time analysis of EEG signals by addressing the limitations of offline processing.
    • To provide a freely available tool for researchers working with real-time EEG.

    Main Methods:

    • Combined online artifact subspace reconstruction (ASR) for transient artifact removal.
    • Integrated online recursive independent component analysis (ORICA) with an independent component (IC) classifier for continuous artifact removal.
    • Validated the pipeline using EEG recordings with movement, muscle, blink, and saccade artifacts.

    Main Results:

    • The proposed pipeline successfully removed large amplitude transient artifacts using ASR.
    • ORICA and IC classification effectively computed, classified, and removed continuous artifactual components.
    • Demonstrated the pipeline's efficacy on diverse artifact types in real EEG data.

    Conclusions:

    • The combined ASR and ORICA pipeline offers a robust solution for online EEG artifact removal.
    • This approach significantly improves the quality of EEG data for real-time analysis.
    • The freely available toolbox facilitates the adoption of real-time artifact correction in neuroscience research.