Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, People's Republic of China.
J Neural Eng. 2022 Jan 24;19(1). doi: 10.1088/1741-2552/ac49a6.
Supernumerary robotic limbs are body augmentation robotic devices by adding extra limbs or fingers to the human body different from the traditional wearable robotic devices such as prosthesis and exoskeleton. We proposed a novel motor imagery (MI)-based brain-computer interface (BCI) paradigm based on the sixth-finger which imagines controlling the extra finger movements. The goal of this work is to investigate the electromyographic (EEG) characteristics and the application potential of MI-based BCI systems based on the new imagination paradigm (the sixth finger MI).Fourteen subjects participated in the experiment involving the sixth finger MI tasks and rest state. Event-related spectral perturbation was adopted to analyze EEG spatial features and key-channel time-frequency features. Common spatial patterns were used for feature extraction and classification was implemented by support vector machine. A genetic algorithm (GA) was used to select combinations of EEG channels that maximized classification accuracy and verified EEG patterns based on the sixth finger MI. And we conducted a longitudinal 4 weeks EEG control experiment based on the new paradigm.Event-related desynchronization (ERD) was found in the supplementary motor area and primary motor area with a faint contralateral dominance. Unlike traditional MI based on the human hand, ERD was also found in frontal lobe. GA results showed that the distribution of the optimal eight-channel is similar to EEG topographical distributions, nearing parietal and frontal lobe. And the classification accuracy based on the optimal eight-channel (the highest accuracy of 80% and mean accuracy of 70%) was significantly better than that based on the random eight-channel (< 0.01).This work provided a new paradigm for MI-based MI system and verified its feasibility, widened the control bandwidth of the BCI system.
多指机器人肢体是通过向人体添加额外的肢体或手指来进行身体增强的机器人设备，与传统的可穿戴机器人设备（如假肢和外骨骼）不同。我们提出了一种基于第六指的新型运动想象（MI）脑机接口（BCI）范式，用于想象控制额外手指的运动。这项工作的目的是研究基于新想象范式（第六指 MI）的 MI 脑机接口系统的肌电（EEG）特征和应用潜力。14 名受试者参与了涉及第六指 MI 任务和休息状态的实验。采用事件相关光谱微扰来分析 EEG 空间特征和关键通道时频特征。常用空间模式用于特征提取，支持向量机用于分类。遗传算法（GA）用于选择最大化分类准确性的 EEG 通道组合，并验证基于第六指 MI 的 EEG 模式。并且我们基于新范式进行了一个为期 4 周的 EEG 控制实验。在补充运动区和初级运动区发现了事件相关去同步（ERD），呈现微弱的对侧优势。与传统基于人手的 MI 不同，额叶也发现了 ERD。GA 结果表明，最优八通道的分布类似于 EEG 地形图分布，接近顶叶和额叶。基于最优八通道的分类准确率（最高准确率为 80%，平均准确率为 70%）明显优于基于随机八通道的分类准确率（<0.01）。这项工作为 MI 脑机接口系统提供了一种新的范式，验证了其可行性，拓宽了 BCI 系统的控制带宽。
