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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...

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Assessment and Communication for People with Disorders of Consciousness

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BrainFusion: a Low-Code, Reproducible, and Deployable Software Framework for Multimodal Brain‒Computer Interface and

Wenhao Li¹, Chenyang Gao¹, Zhaobo Li¹

¹School of Biomedical Science and Engineering, South China University of Technology, Guangzhou, 511442, China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)

|June 5, 2025

Summary

BrainFusion enhances multimodal brain-computer interface research by standardizing data and automating analysis. This unified framework improves reproducibility and real-world application of brain-body interaction studies.

Keywords:

application deployment brain‒body interactions brain‒computer interface multimodal physiological signals neurovascular coupling

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Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

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Published on: July 21, 2021

Area of Science:

Neuroscience and Biomedical Engineering
Computational Neuroscience
Rehabilitation Engineering

Background:

Multimodal brain-computer interface (BCI) research faces challenges in data integration, standardization, and real-world deployment.
Existing analytical complexities hinder the translation of advanced electroencephalography (EEG)-based BCIs.
Limited accessibility and a gap between research and practical application impede progress in brain-body interaction.

Purpose of the Study:

To introduce BrainFusion, a unified software framework for reproducible multimodal BCI and brain-body interaction research.
To address the analytical complexity and standardization issues in integrating diverse physiological signals.
To facilitate the development and deployment of real-world BCI applications.

Main Methods:

Development of a unified software framework with standardized data structures and automated preprocessing pipelines.
Implementation of cross-modal feature engineering and integrated machine learning modules.
Creation of an application generator for streamlined deployment of BCI workflows as standalone executables.

Main Results:

Achieved 95.5% accuracy in within-subject EEG-fNIRS motor imagery classification using ensemble modeling.
Attained 80.2% accuracy in EEG-ECG sleep staging using deep learning, with successful deployment as an executable tool.
Supported integration of EEG, fNIRS, EMG, and ECG data within a low-code, visual environment.

Conclusions:

BrainFusion effectively bridges the gap between multimodal BCI research and real-world applications.
The framework enhances reproducibility, accessibility, and translational potential in brain-body interaction studies.
BrainFusion offers a standardized, low-code solution for complex multimodal physiological signal analysis.