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    This study introduces a novel brain-machine interface (BMI) using electroencephalogram (EEG) signals to decode neural languages from speech imagery. The system enables real-time, human-like communication and interaction for various cooperative tasks.

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    Area of Science:

    • Neuroscience
    • Human-Computer Interaction
    • Artificial Intelligence

    Background:

    • Brain-machine interfaces (BMIs) using electroencephalogram (EEG) are emerging tools for motor function restoration and human-machine communication.
    • Neurolinguistic research explores EEG for naturalistic human-machine interaction by decoding neural signals related to speech imagery.

    Purpose of the Study:

    • To investigate the feasibility of a deep neurolinguistic learning model for decoding neural languages directly from speech imagery.
    • To evaluate the performance of an EEG-based BMI in facilitating real-time cooperative tasks among multiple users.

    Main Methods:

    • Development of a deep neurolinguistic learning model to interpret neural signals associated with speech imagery.
    • Real-time experimental validation of the BMI system for diverse cooperative tasks, including essential activities, collaborative play, and emotional interactions.

    Main Results:

    • Successful decoding of neural languages from speech imagery using the proposed deep neurolinguistic learning model.
    • Demonstration of a functional BMI system capable of supporting various real-time cooperative scenarios among multiple users.
    • Validation of the BMI's potential for human-like intelligence interaction and expanded communication paradigms.

    Conclusions:

    • The developed EEG-based BMI system offers a novel approach to real-time, human-like communication and interaction.
    • This research extends the capabilities of brain-machine interfaces into complex cooperative tasks and emotional interactions.
    • The findings open new frontiers for BMI technology in bridging human intelligence and machine communication.