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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Signal Processing

    Background:

    • Electroencephalography/Magnetoencephalography source imaging (ESI) provides high spatial and temporal resolution for noninvasive brain activity measurement.
    • The portability of Electroencephalography (EEG) makes it particularly attractive for diverse applications like brain-computer interfaces (BCI), neuromarketing, and neuroergonomics.
    • Existing reviews on ESI often lack a comprehensive overview of recent advancements and present models in isolation.

    Purpose of the Study:

    • To provide a comprehensive review of state-of-the-art ESI models, detailing their neurophysiological assumptions and mathematical underpinnings.
    • To outline key applications of ESI, implementation steps, current challenges, and future research directions.
    • To offer practical implementation guidance for various ESI models, including access to open-source datasets and code.

    Main Methods:

    • Systematic review of existing literature on ESI models.
    • Analysis of neurophysiological assumptions and mathematical derivations of ESI techniques.
    • Compilation of ESI applications, implementation strategies, and challenges.
    • Demonstration of practical ESI model usage with open-source resources.

    Main Results:

    • A structured overview of widely-used and advanced ESI models is presented.
    • Key applications, implementation hurdles, and potential future research avenues in ESI are identified.
    • Practical implementation details and open-source tools are provided for representative ESI models.

    Conclusions:

    • ESI is a rapidly evolving field with significant potential for advancing our understanding of brain dynamics.
    • The review aims to consolidate knowledge and facilitate further research and application development in ESI.
    • Widespread adoption of ESI technologies is anticipated to revolutionize fields such as BCI and neuroergonomics.