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    This study introduces a partial domain adaptation (PDA) framework to stabilize Brain-Computer Interface (BCI) decoding by aligning task-relevant neural signals. PDA enhances long-term decoding reliability for chronic neural rehabilitation applications.

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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Machine Learning

    Background:

    • Brain-Computer Interfaces (BCI) show promise for neural rehabilitation.
    • Non-stationary neural signals cause decoding instability, hindering chronic BCI use.
    • Existing domain adaptation methods struggle with task-irrelevant neural components.

    Purpose of the Study:

    • To develop a method for stable neural alignment in BCIs.
    • To address the challenge of decoding instability caused by non-stationary neural signals.
    • To improve the reliability of BCIs for chronic applications.

    Main Methods:

    • Proposed a novel partial domain adaptation (PDA) framework.
    • Constructed a latent space using a causal dynamical system for flexible decoding.
    • Disentangled task-relevant features using VAE-based representation learning and adversarial alignment.

    Main Results:

    • Analytically validated improved stability of neural representations using Lyapunov theory.
    • Demonstrated significant enhancement in cross-session decoding performance across various neural datasets.
    • Achieved stable neural representations across experimental days for reliable long-term decoding.

    Conclusions:

    • PDA enables stable neural representations, crucial for reliable long-term BCI decoding.
    • This approach offers a novel solution for chronic reliability in real-world BCI deployments.