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Related Experiment Video

Updated: Jan 9, 2026

A Method for Systematic Electrochemical and Electrophysiological Evaluation of Neural Recording Electrodes
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RISE-iEEG: Robust to Inter-Subject Electrodes Implantation Variability iEEG Classifier.

Maryam Ostadsharif Memar, Navid Ziaei, Behzad Nazari

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
    Summary

    We developed RISE-iEEG, a novel intracranial electroencephalography (iEEG) decoder model. It overcomes electrode implantation variability, improving neural decoding accuracy across participants for brain-computer interfaces.

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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Machine Learning

    Background:

    • Intracranial electroencephalography (iEEG) offers high-resolution neural data for clinical use and brain-computer interfaces (BCIs).
    • Inter-subject variability in electrode placement hinders the development of generalized neural decoders.
    • Existing models struggle to account for anatomical differences in electrode implantation sites.

    Purpose of the Study:

    • To introduce a novel deep learning model, RISE-iEEG, designed to be robust to inter-subject variability in iEEG electrode implantation.
    • To develop a generalized neural decoder that does not require precise electrode coordinates for each participant.
    • To improve the accuracy and generalizability of neural decoding from iEEG data.

    Main Methods:

    • Developed RISE-iEEG (Robust to Inter-Subject Electrode Implantation Variability iEEG Classifier), a deep neural network.
    • Incorporated a participant-specific projection network to map individual neural data onto a common low-dimensional space.
    • Evaluated RISE-iEEG on the Music Reconstruction and AJILE12 datasets, comparing its performance against established models like HTNet and EEGNet.

    Main Results:

    • RISE-iEEG demonstrated superior performance compared to HTNet and EEGNet, achieving an average F1 score of 0.83, approximately 7% higher.
    • The model successfully compensated for electrode implantation variability without needing electrode coordinates.
    • Analysis of projection network weights identified the Superior Temporal and Postcentral lobes as key encoding regions for the tested datasets.

    Conclusions:

    • RISE-iEEG offers a robust and generalizable solution for neural decoding from iEEG data, effectively handling inter-subject variability.
    • The model enhances decoding accuracy while preserving interpretability, making it valuable for clinical applications and BCIs.
    • The findings highlight the potential of adaptive projection networks in overcoming anatomical differences for cross-subject neural decoding.