BrainFusion: a Low‐Code, Reproducible, and Deployable Software Framework for Multimodal Brain‒Computer Interface and Brain‒Body Interaction Research

作者：Wenhao Li, Chenyang Gao, Zhaobo Li, Yunheng Diao, Jiaxin Li, Jiayi Zhou, Jing Zhou, Ying Peng, Guanchu Chen, Xuecheng Wu, Kai Wu · 发表于：Advanced Science · 年份：2025 · DOI：10.1002/advs.202417408 · 被引用次数：6 · 研究领域：EEG and Brain-Computer Interfaces、Functional Brain Connectivity Studies、ECG Monitoring and Analysis

This study presents BrainFusion, a unified software framework designed to improve reproducibility and support translational applications in multimodal brain-computer interface (BCI) and brain-body interaction research. While electroencephalography (EEG)-based BCIs have advanced considerably, integrating multimodal physiological signals remains hindered by analytical complexity, limited standardization, and challenges in real-world deployment. BrainFusion addresses these gaps through standardized data structures, automated preprocessing pipelines, cross-modal feature engineering, and integrated machine learning modules. Its application generator further enables streamlined deployment of workflows as standalone executables. Demonstrated in two case studies, BrainFusion achieves 95.5% accuracy in within-subject EEG-functional near-infrared spectroscopy (fNIRS) motor imagery classification using ensemble modeling and 80.2% accuracy in EEG-electrocardiography (ECG) sleep staging using deep learning, with the latter successfully deployed as an executable tool. Supporting EEG, fNIRS, electromyography (EMG), and ECG, BrainFusion provides a low-code, visually guided environment, facilitating accessibility and bridging the gap between multimodal research and application in real world.