使用无创脑机接口控制肌电假肢的抓握类型区分变异性

Corentin Piozin, Lisa Bouarroudj, Jean-Yves Audran, Brice Lavrard, Catherine Simon, Florian Waszak, Selim Eskiizmirliler
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摘要

利用脑电图(EEG)活动对来自同一肢体的多个动作进行解码是上肢截肢者控制假肢所面临的一项关键挑战。本研究调查了通过脑电信号控制改进型 Myobock 假肢的四个手部动作的分类。我们报告了四名截肢者和二十名健全受试者在三种条件下进行四个抓握动作的三次脑电图记录结果:运动执行(ME)、运动想象(MI)和运动观察(MO)。脑电图预处理后,使用通用空间模式(CSP)、小波分解(WD)和黎曼几何进行特征提取。各种分类算法被用于解码脑电信号,并对模式可分性进行了评估。我们评估了不同电极组合的系统性能,并与原始设置进行了比较。结果表明,区分有运动和无运动的准确率达到了 100%,而运动之间的分类准确率达到了 70-90%。在分类性能方面,不同记录条件之间没有发现明显的差异。健全参与者的表现优于截肢者,但在运动想象方面没有明显差异。各次训练的成绩都没有提高,而且在脑电图模式区分方面存在相当大的差异。将电极数量减少一半仅导致平均准确率下降 2%。这些结果为开发可穿戴脑机接口,尤其是电极优化和抓握动作分类训练提供了启示。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Variability in Grasp Type Distinction for Myoelectric Prosthesis Control Using a Non-Invasive Brain-Machine Interface
Decoding multiple movements from the same limb using electroencephalographic (EEG) activity is a key challenge with applications for controlling prostheses in upper-limb amputees. This study investigates the classification of four hand movements to control a modified Myobock prosthesis via EEG signals. We report results from three EEG recording sessions involving four amputees and twenty able-bodied subjects performing four grasp movements under three conditions: Motor Execution (ME), Motor Imagery (MI), and Motor Observation (MO). EEG preprocessing was followed by feature extraction using Common Spatial Patterns (CSP), Wavelet Decomposition (WD), and Riemannian Geometry. Various classification algorithms were applied to decode EEG signals, and a metric assessed pattern separability. We evaluated system performance across different electrode combinations and compared it to the original setup. Our results show that distinguishing movement from no movement achieved 100% accuracy, while classification between movements reached 70-90%. No significant differences were found between recording conditions in classification performance. Able-bodied participants outperformed amputees, but there were no significant differences in Motor Imagery. Performance did not improve across the sessions, and there was considerable variability in EEG pattern distinction. Reducing the number of electrodes by half led to only a 2% average accuracy drop. These results provide insights into developing wearable brain-machine interfaces, particularly for electrode optimization and training in grasp movement classification.
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