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Research Progress on Motor Imagery Decoding in Brain-Computer Interface Based on EEG-fNIRS Multimodal Fusion

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Systematic Review Open Access

Research Progress on Motor Imagery Decoding in Brain-Computer Interface Based on EEG-fNIRS Multimodal Fusion

Zhongpeng Wang 1,2,3 Jing Liu 1 Yijie Zhou 1,2,3,4 Long Chen 1,2,3,* Dong Ming 1,2,3

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1
Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China
2
Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration, Tianjin 300392, China
3
State Key Laboratory of Advanced Medical Materials and Devices, Tianjin University, Tianjin 300072, China
4
School of Disaster and Emergency Medicine, Tianjin University, Tianjin 300072, China
*
Authors to whom correspondence should be addressed.

Received: 12 June 2026 Revised: 14 July 2026 Accepted: 28 July 2026 Published: 06 August 2026

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© 2026 The authors. This is an open access article under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).

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Intell. Rehabil. Eng. 2026, 1(1), 10008; DOI: 10.70322/ire.2026.10008
ABSTRACT: Motor imagery-based brain-computer interface (MI-BCI) decodes subjective motor intentions to achieve proactive output control of external devices, representing a core research direction in BCI with important applications in post-stroke motor rehabilitation. Neural signals for motor imagery can be recorded using electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). Combining the high temporal resolution of EEG with the high spatial resolution of fNIRS through multimodal fusion enhances the decoding accuracy of motor imagery commands and provides comprehensive insights into brain dynamics. This paper systematically reviews EEG-fNIRS multimodal fusion strategies and representative algorithms from traditional machine learning (ML) and deep learning (DL) for decoding fused data. We also discuss current challenges and potential solutions to facilitate MI-BCI research and support future BCI-related industries.
Keywords: Brain-computer interface; Electroencephalography; Functional near-infrared spectroscopy; Motor imagery; Multimodal fusion

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