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Recording Human Electrocorticographic ECoG Signals for Neuroscientific Research and Real-time Functional Cortical Mapping
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Inferring preoperative cognitive function from intraoperative electroencephalography in elderly patients using

Juan C Pedemonte, Haoqi Sun, Isaac G Freedman

    IEEE Transactions on Bio-Medical Engineering
    |March 6, 2026
    PubMed
    Summary
    This summary is machine-generated.

    Machine learning models using intraoperative electroencephalography (EEG) can infer preoperative cognitive function in older surgical patients. While correlations were weak, this approach shows potential for early detection of neurocognitive disorders.

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    Area of Science:

    • Neuroscience
    • Medical Informatics
    • Machine Learning

    Background:

    • Cognitive decline in older adults post-surgery is a significant concern.
    • Accurate assessment of preoperative cognitive function is crucial for risk stratification and personalized care.
    • Intraoperative electroencephalography (EEG) offers a continuous physiological signal during surgery.

    Purpose of the Study:

    • To develop and evaluate machine learning (ML) models for inferring preoperative cognitive function from intraoperative EEG.
    • To assess the generalizability of these ML models across different surgical populations and geographical locations.

    Main Methods:

    • Retrospective analysis of two independent cohorts (USA and Chile) comprising patients over 60 undergoing surgery.
    • Utilized intraoperative EEG data and preoperative Montreal Cognitive Assessment (MoCA) scores.
    • Trained and tested four ML models: logistic regression (L2), random forest, gradient boosting tree, and extreme gradient boosting.

    Main Results:

    • A logistic regression model with L2 regularization demonstrated the best performance in the training dataset (WRMSE 2.82, Spearman's rho 0.18).
    • This model's performance generalized to the external testing dataset (WRMSE 2.72, Spearman's rho 0.14), indicating low error (<3 MoCA points).
    • Weak monotonic correlations suggest limitations in capturing consistent relationships between EEG-inferred and actual cognitive scores.

    Conclusions:

    • ML models trained on intraoperative EEG can infer preoperative cognitive function in older surgical patients with acceptable error.
    • The models show generalizability across distinct populations, but correlations are weak.
    • Further validation is needed for clinical implementation to aid in early detection and mitigation of neurocognitive disorders.