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Updated: Jan 23, 2026

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    Area of Science:

    • Neuroscience and Neuroimaging
    • Artificial Intelligence in Medicine

    Background:

    • Neuroimaging techniques like functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) are crucial for studying brain activity.
    • The integration of artificial intelligence (AI) offers advanced capabilities for analyzing complex neuroimaging data.

    Purpose of the Study:

    • To investigate various AI-driven techniques, including machine learning (ML) and deep learning (DL), for brain exploration using fMRI and EEG data.
    • To provide a comprehensive overview of AI applications in neuroimaging for cognitive neuroscience and medical diagnostics.

    Main Methods:

    • Utilizing machine learning (ML) and deep learning (DL) models to interpret neural activities from fMRI and EEG data.
    • Analyzing AI-based models for pattern recognition and abnormality detection in brain imaging.

    Main Results:

    • AI models demonstrate high accuracy in identifying patterns and detecting abnormalities in brain activity.
    • AI applications show significant potential in brain decoding, cognitive state monitoring, brain-computer interfaces (BCI), and disease diagnosis.

    Conclusions:

    • AI, particularly ML and DL, is revolutionizing neuroimaging by enhancing diagnostic accuracy and research capabilities.
    • Future directions involve further exploration of AI's transformative impact on understanding the human brain and neurological conditions.