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    Area of Science:

    • Signal Processing
    • Neuroscience
    • Machine Learning

    Background:

    • Non-invasive electroencephalography (EEG) data is complex and often irregularly sampled.
    • Graph signal processing (GSP) offers tools for analyzing such data.
    • Motor imagery (MI) decoding is crucial for brain-computer interfaces (BCIs).

    Purpose of the Study:

    • To exploit Graph Slepian functions for robust decoding of motor imagery (MI) brain activity.
    • To introduce a data-driven, subject-specific design for Graph Slepian functions using contrastive learning.
    • To enhance spatial filtering for improved MI decoding accuracy.

    Main Methods:

    • Utilized Graph Slepian functions, building upon the graph Fourier transform (GFT).
    • Developed a contrastive learning pipeline for subject-specific Graph Slepian function design.
    • Integrated these functions as spatial filters in a motor imagery decoding scheme using a support vector machine (SVM).

    Main Results:

    • The proposed method demonstrated superior performance against popular alternatives in MI decoding on two public datasets.
    • Graph Slepian functions enhanced the information from multichannel EEG signals, relating to the participant's intention.
    • The technique showed computational efficiency due to simple matrix operations.

    Conclusions:

    • The data-driven design of Graph Slepian functions provides effective spatial filtering for MI decoding.
    • This approach offers a robust and computationally efficient solution for brain-computer interfaces.
    • The method holds potential for real-time applications in BCI systems.