Ma Shuangling, Situ Zijie, Peng Xiaobo, Li Zhangyang, Huang Ying
College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China.
Shenzhen Key Laboratory of Marine Bioresources and Ecology, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen 518060, China.
Biomimetics (Basel). 2025 Jul 9;10(7):452. doi: 10.3390/biomimetics10070452.
Brain-Computer Interfaces (BCIs) enable direct communication between the brain and external devices by decoding motor intentions from EEG signals. However, the existing multi-class classification methods for motor imagery EEG (MI-EEG) signals are hindered by low signal quality and limited accuracy, restricting their practical application. This study focuses on rehabilitation training scenarios, aiming to capture the motor intentions of patients with partial or complete motor impairments (such as stroke survivors) and provide feedforward control commands for exoskeletons. This study developed an EEG acquisition protocol specifically for use with lower-limb rehabilitation motor imagery (MI). It systematically explored preprocessing techniques, feature extraction strategies, and multi-classification algorithms for multi-task MI-EEG signals. A novel 3D EEG convolutional neural network (3D EEG-CNN) that integrates time/frequency features is proposed. Evaluations on a self-collected dataset demonstrated that the proposed model achieved a peak classification accuracy of 66.32%, substantially outperforming conventional approaches and demonstrating notable progress in the multi-class classification of lower-limb motor imagery tasks.
脑机接口（BCIs）通过从脑电图（EEG）信号中解码运动意图，实现大脑与外部设备之间的直接通信。然而，现有的用于运动想象脑电（MI-EEG）信号的多类分类方法受到信号质量低和准确性有限的阻碍，限制了它们的实际应用。本研究聚焦于康复训练场景，旨在捕捉部分或完全运动功能受损患者（如中风幸存者）的运动意图，并为外骨骼提供前馈控制命令。本研究开发了一种专门用于下肢康复运动想象（MI）的脑电采集协议。它系统地探索了多任务MI-EEG信号的预处理技术、特征提取策略和多类分类算法。提出了一种集成时间/频率特征的新型三维脑电卷积神经网络（3D EEG-CNN）。在自行采集的数据集上进行的评估表明，所提出的模型实现了66.32%的峰值分类准确率，显著优于传统方法，并在下肢运动想象任务的多类分类方面取得了显著进展。
