Chenyang Li ^1,2 Yuchen Xu ³ Tao Feng ^1,2 Minmin Wang ^1,2,4 Xiaomei Zhang ⁵ Li Zhang ⁵ Ruidong Cheng ⁶ Weihai Chen ^7,8 Weidong Chen ^1,2* Shaomin Zhang ^1,2

• ¹ Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, Zhejiang Province, China
• ² Key Laboratory of Biomedical Engineering of Education Ministry, Department of Biomedical Engineering, Zhejiang University, Hangzhou, China
• ³ Center of Excellence in Biomedical Research on Advanced Integrated-on-Chips Neurotechnologies (CenBRAIN Neurotech), School of Engineering, Westlake University, Hangzhou, China
• ⁴ Westlake Institute for Optoelectronics, Westlake University, Hangzhou, China
• ⁵ First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China
• ⁶ Center for Rehabilitation Medicine, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital, Hangzhou, Jiangsu Province, China
• ⁷ School of Automation Science and Electrical Engineering, Beihang University, Beijing, Beijing, China
• ⁸ Hangzhou Innovation Institute, Beihang University, Hangzhou, Jiangsu Province, China

Introduction: Rehabilitation devices assist individuals with movement disorders by supporting daily activities and facilitating effective rehabilitation training. Accurate and early motor intention detection is vital for real-time device applications. However, traditional methods of motor intention detection often rely on single-mode signals, such as EEG or EMG alone, which can be limited by low signal quality and reduced stability. This study proposes a multimodal fusion method based on EEG-EMG functional connectivity to detect sitting and standing intentions before movement execution, enabling timely intervention and reducing latency in rehabilitation devices.Methods: Eight healthy subjects and five spinal cord injury (SCI) patients performed cue-based sit-tostand and stand-to-sit transition tasks while EEG and EMG data were recorded simultaneously. We constructed EEG-EMG functional connectivity networks using data epochs from the 1.5-second period prior to movement onset. Pairwise spatial filters were then designed to extract discriminative spatial network topologies. Each filter paired with a support vector machine classifier to classify future movements into one of three classes: sit-to-stand, stand-to-sit, or rest. The final prediction was determined using a majority voting scheme.Results: Among the three functional connectivity methods investigatedcoherence, Pearson correlation coefficient and mutual information (MI)the MI-based EEG-EMG network showed the highest decoding performance (94.33%), outperforming both EEG (73.89%) and EMG (89.16%). The robustness of the fusion method was further validated through a fatigue training experiment with healthy subjects. The fusion method achieved 92.87% accuracy during the post-fatigue stage, with no significant difference compared to the pre-fatigue stage (p > 0.05). Additionally, the proposed method using pre-movement windows achieved accuracy comparable to trans-movement windows (p > 0.05 for both pre-and post-fatigue stages). For the SCI patients, the fusion method showed improved accuracy, achieving 87.54% compared to single-modality methods (EEG: 83.03%, EMG: 84.13%), suggesting that the fusion method could be promising for practical rehabilitation applications. Conclusion: Our results demonstrated that the proposed multimodal fusion method significantly enhances the performance of detecting human motor intentions. By enabling early detection of sitting and standing intentions, this method holds the potential to offer more accurate and timely interventions within rehabilitation systems.