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Developing Lightweight Models with Data Optimization for Attending Speaker Identity from EEG without Spatial

Yuting Ding, Lei Wang, Xuefei Wang

    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
    This summary is machine-generated.

    This study shows electroencephalography (EEG) signals can decode auditory attention to a target speaker without eye gaze artifacts. A novel EEG-Mixup method and lightweight model improve accuracy and efficiency for brain-computer interfaces (BCIs).

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    Area of Science:

    • Neuroscience
    • Signal Processing
    • Machine Learning

    Background:

    • Spatial auditory attention decoding (Sp-AAD) is crucial for brain-computer interfaces (BCIs).
    • Previous Sp-AAD methods often rely on eye gaze artifacts, not true auditory attention.
    • This research investigates EEG signal discriminability for target speaker identity, excluding eye gaze influence.

    Purpose of the Study:

    • To verify if EEG signals possess sufficient features for decoding auditory attention to a specific speaker.
    • To develop and validate a method to mitigate eye gaze artifacts in Sp-AAD.
    • To create a computationally efficient model for Sp-AAD.

    Main Methods:

    • Proposed an EEG-Mixup data optimization technique to adjust data distribution and generate soft labels, suppressing trial-specific features.
    • Developed a lightweight EEG-MLP model with approximately 2.5k parameters.
    • Evaluated model performance against the state-of-the-art DenseNet-3D model in cross-trial scenarios.

    Main Results:

    • The EEG-Mixup method significantly improved model generalization without increasing data volume.
    • The lightweight EEG-MLP model outperformed DenseNet-3D in cross-trial performance.
    • The proposed model demonstrated enhanced computational efficiency and inference speed.

    Conclusions:

    • EEG signals contain discriminative features for target speaker identity decoding, independent of eye gaze.
    • Data optimization techniques like EEG-Mixup can enhance BCI performance.
    • Lightweight models offer a practical and efficient approach for future auditory BCI systems.