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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...

Vision

Vision

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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Updated: Sep 11, 2025

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

BrainVision: Cross-domain EEG decoding for visual content retrieval and reconstruction.

Ting Xu¹, Lianzhi Yu¹, Yongwei Zheng¹

¹University of Shanghai for Science and Technology, Shanghai, 200093, China.

|August 15, 2025

Summary

BrainVision integrates visual and emotional brain data for advanced visual content generation. This cross-domain approach enhances brain decoding for better brain-computer interfaces.

Keywords:

Cross-domain learning Electroencephalogram Large language models Zero-shot

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Last Updated: Sep 11, 2025

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

Topographical Estimation of Visual Population Receptive Fields by fMRI

Topographical Estimation of Visual Population Receptive Fields by fMRI

Published on: February 3, 2015

Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns

Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns

Published on: May 12, 2019

Area of Science:

Neuroscience and Artificial Intelligence
Brain-Computer Interfaces
Machine Learning

Background:

Decoding human visual intent from brain signals is challenging due to isolated datasets and limited generalization.
Current brain decoding methods struggle to integrate information from diverse neural sources and modalities.

Purpose of the Study:

To introduce BrainVision, a novel framework for cross-domain learning that bridges visual recognition and emotional electroencephalography (EEG) datasets.
To enable comprehensive visual content generation by aligning neural patterns from heterogeneous EEG sources into a shared representation space.

Main Methods:

Implemented a unified cross-domain alignment strategy to map neural patterns from THINGS-EEG and DEAP datasets.
Enabled visual content generation through content retrieval, linguistic descriptions via adapter-enhanced LLMs, and image reconstruction using stable diffusion models.

Main Results:

BrainVision significantly outperformed single-domain approaches, with a 15.3% increase in retrieval accuracy and a 12.7% improvement in image reconstruction.
Demonstrated robust zero-shot generalization, maintaining 82% performance on unseen stimuli.
Multi-modal outputs provided complementary interpretations of neural activity, enhancing understanding of visual intent.

Conclusions:

Integrating diverse neural datasets substantially enhances brain decoding capabilities.
BrainVision offers a promising direction for developing more intuitive and versatile brain-computer interfaces.
The framework bridges the gap between neural activity and rich visual experiences across cognitive domains.