Chair in Brain-Machine Interface (CNBI), Center for Neuroprosthetics (CNP), Ecole Polytechnique Federale de Lausanne (EPFL), Chemin des Mines 9, 1202, Geneva, Switzerland.
Chair in Brain-Machine Interface (CNBI), Center for Neuroprosthetics (CNP), Ecole Polytechnique Federale de Lausanne (EPFL), Chemin des Mines 9, 1202, Geneva, Switzerland.
Neuroimage. 2018 Aug 1;176:268-276. doi: 10.1016/j.neuroimage.2018.04.005. Epub 2018 Apr 22.
Motor imagery (MI) has been largely studied as a way to enhance motor learning and to restore motor functions. Although it is agreed that users should emphasize kinesthetic imagery during MI, recordings of MI brain patterns are not sufficiently reliable for many subjects. It has been suggested that the usage of somatosensory feedback would be more suitable than standardly used visual feedback to enhance MI brain patterns. However, somatosensory feedback should not interfere with the recorded MI brain pattern. In this study we propose a novel feedback modality to guide subjects during MI based on sensory threshold neuromuscular electrical stimulation (St-NMES). St-NMES depolarizes sensory and motor axons without eliciting any muscular contraction. We hypothesize that St-NMES does not induce detectable ERD brain patterns and fosters MI performance. Twelve novice subjects were included in a cross-over design study. We recorded their EEG, comparing St-NMES with visual feedback during MI or resting tasks. We found that St-NMES not only induced significantly larger desynchronization over sensorimotor areas (p<0.05) but also significantly enhanced MI brain connectivity patterns. Moreover, classification accuracy and stability were significantly higher with St-NMES. Importantly, St-NMES alone did not induce detectable artifacts, but rather the changes in the detected patterns were due to an increased MI performance. Our findings indicate that St-NMES is a promising feedback in order to foster MI performance and cold be used for BMI online applications.
运动想象 (MI) 在很大程度上被研究为一种增强运动学习和恢复运动功能的方法。尽管人们普遍认为用户在进行 MI 时应强调动觉想象，但由于许多受试者的记录不够可靠，因此 MI 脑模式的记录并不充分。有人建议，使用体感反馈比标准视觉反馈更适合增强 MI 脑模式。然而，体感反馈不应干扰记录的 MI 脑模式。在这项研究中，我们提出了一种新的反馈方式，基于感觉阈值神经肌肉电刺激 (St-NMES) 在 MI 期间指导受试者。St-NMES 去极化感觉和运动轴突，而不会引起任何肌肉收缩。我们假设 St-NMES 不会引起可检测的 ERD 脑模式，并促进 MI 性能。12 名新手受试者参与了一项交叉设计研究。我们记录了他们的 EEG，在 MI 或休息任务期间将 St-NMES 与视觉反馈进行比较。我们发现，St-NMES 不仅在感觉运动区域引起了显著更大的去同步化（p<0.05），而且还显著增强了 MI 脑连接模式。此外，St-NMES 的分类准确性和稳定性都显著提高。重要的是，St-NMES 本身不会引起可检测的伪影，而是由于 MI 性能的提高导致检测到的模式发生变化。我们的研究结果表明，St-NMES 是一种有前途的反馈方式，可以促进 MI 性能，并可用于 BMI 的在线应用。
