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Related Concept Videos

Vision01:24

Vision

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Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
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Decoding Visual Imagination and Perception from EEG via Topomap Sequences.

Hossein Ahmadi, Luca Mesin

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel EEG decoding framework using Topomaps to differentiate visual imagination from perception. The method achieves high accuracy in data-scarce conditions, revealing distinct neural patterns for these cognitive states.

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    Area of Science:

    • Neuroscience
    • Cognitive Science
    • Biomedical Engineering

    Background:

    • Distinguishing between visual imagination and perception is crucial for understanding cognitive processes.
    • Existing electroencephalography (EEG) decoding methods may overlook critical spatiotemporal patterns.
    • Data scarcity poses a significant challenge in developing robust EEG-based brain-computer interfaces (BCIs).

    Purpose of the Study:

    • To develop and validate a Topomap-based EEG decoding framework for differentiating pictorial imagination from perception.
    • To assess the framework's performance in data-scarce scenarios using a leave-one-subject-out (LOSO) cross-validation.
    • To explore the potential of Topomaps as effective EEG feature representations for cognitive state decoding.

    Main Methods:

    • EEG signals were converted into dense sequences of scalp voltage maps (Topomaps).
    • A convolutional neural network (CNN) with squeeze-and-excitation (SE) blocks was applied to Topomap sequences.
    • A leave-one-subject-out (LOSO) cross-validation scheme was employed with a single trial per subject.

    Main Results:

    • The proposed framework achieved 95.1% accuracy in distinguishing imagination from perception under data-scarce conditions.
    • Results demonstrated clear neural distinctions between imagination and perception states.
    • The study confirmed the viability and generalizability of Topomaps for EEG feature representation.

    Conclusions:

    • Topomap-based EEG decoding offers a robust method for differentiating visual imagination and perception, even with limited data.
    • The framework has potential applications in enhancing diagnostic tools for cognitive disorders.
    • Future work could extend this approach to other modalities and advanced deep learning architectures for improved BCI applications.