Li Chenyang, Xu Yuchen, Feng Tao, Wang Minmin, Zhang Xiaomei, Zhang Li, Cheng Ruidong, Chen Weihai, Chen Weidong, Zhang Shaomin
Key Laboratory of Biomedical Engineering of Ministry of Education, Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China.
Department of Biomedical Engineering, Zhejiang University, Hangzhou, China.
Front Neurosci. 2025 Jan 24;19:1532099. doi: 10.3389/fnins.2025.1532099. eCollection 2025.
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.
Eight healthy subjects and five spinal cord injury (SCI) patients performed cue-based sit-to-stand 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-s 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.
Among the three functional connectivity methods investigated-coherence, 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 ( > 0.05). Additionally, the proposed method using pre-movement windows achieved accuracy comparable to trans-movement windows ( > 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.
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.
康复设备通过支持日常活动和促进有效的康复训练来协助患有运动障碍的个体。准确且早期的运动意图检测对于实时设备应用至关重要。然而，传统的运动意图检测方法通常仅依赖于单模态信号，例如单独的脑电图（EEG）或肌电图（EMG），这可能会受到信号质量低和稳定性降低的限制。本研究提出了一种基于EEG-EMG功能连接的多模态融合方法，用于在运动执行前检测坐立意图，从而实现及时干预并减少康复设备中的延迟。
八名健康受试者和五名脊髓损伤（SCI）患者在执行基于提示的坐立和站立转换任务时，同时记录EEG和EMG数据。我们使用运动开始前1.5秒期间的数据片段构建EEG-EMG功能连接网络。然后设计成对的空间滤波器以提取有区分性的空间网络拓扑结构。每个滤波器与支持向量机分类器配对，将未来的运动分为三类之一：坐立、站立到坐下或休息。最终预测通过多数投票方案确定。
在所研究的三种功能连接方法——相干性、皮尔逊相关系数和互信息（MI）中，基于MI的EEG-EMG网络显示出最高的解码性能（94.33%），优于EEG（73.89%）和EMG（89.16%）。通过对健康受试者进行疲劳训练实验，进一步验证了融合方法的稳健性。融合方法在疲劳后阶段的准确率达到92.87%，与疲劳前阶段相比无显著差异（>0.05）。此外，所提出的使用运动前窗口的方法实现的准确率与运动中窗口相当（疲劳前和疲劳后阶段均>0.05）。对于SCI患者，融合方法显示出更高的准确率，达到87.54%，优于单模态方法（EEG：83.03%，EMG：84.13%），这表明融合方法在实际康复应用中可能很有前景。
我们的结果表明，所提出的多模态融合方法显著提高了检测人类运动意图的性能。通过能够早期检测坐立意图，该方法有潜力在康复系统中提供更准确和及时的干预。
