Department of Computer Engineering, Wilhelm-Schickard-Institute, University of Tübingen, Sand 14, 72076, Tübingen, Germany.
Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Silcherstr. 5, 72076, Tübingen, Germany.
J Neuroeng Rehabil. 2018 Nov 20;15(1):110. doi: 10.1186/s12984-018-0438-z.
Brain machine interface (BMI) technology has demonstrated its efficacy for rehabilitation of paralyzed chronic stroke patients. The critical component in BMI-training consists of the associative connection (contingency) between the intention and the feedback provided. However, the relationship between the BMI design and its performance in stroke patients is still an open question.
In this study we compare different methodologies to design a BMI for rehabilitation and evaluate their effects on movement intention decoding performance. We analyze the data of 37 chronic stroke patients who underwent 4 weeks of BMI intervention with different types of association between their brain activity and the proprioceptive feedback. We simulate the pseudo-online performance that a BMI would have under different conditions, varying: (1) the cortical source of activity (i.e., ipsilesional, contralesional, bihemispheric), (2) the type of spatial filter applied, (3) the EEG frequency band, (4) the type of classifier; and also evaluated the use of residual EMG activity to decode the movement intentions.
We observed a significant influence of the different BMI designs on the obtained performances. Our results revealed that using bihemispheric beta activity with a common average reference and an adaptive support vector machine led to the best classification results. Furthermore, the decoding results based on brain activity were significantly higher than those based on muscle activity.
This paper underscores the relevance of the different parameters used to decode movement, using EEG in severely paralyzed stroke patients. We demonstrated significant differences in performance for the different designs, which supports further research that should elucidate if those approaches leading to higher accuracies also induce higher motor recovery in paralyzed stroke patients.
脑机接口（BMI）技术已被证明对瘫痪慢性中风患者的康复具有疗效。BMI 训练的关键组成部分是意图与提供的反馈之间的关联（偶然性）。然而，BMI 设计与其在中风患者中的性能之间的关系仍然是一个悬而未决的问题。
在这项研究中，我们比较了不同的方法来设计用于康复的 BMI，并评估了它们对运动意图解码性能的影响。我们分析了 37 名慢性中风患者的数据，这些患者在 4 周的 BMI 干预过程中，其大脑活动与本体感受反馈之间存在不同类型的关联。我们模拟了 BMI 在不同条件下的伪在线性能，这些条件包括：（1）活动的皮质源（即同侧、对侧、双侧）；（2）应用的空间滤波器类型；（3）EEG 频带；（4）分类器类型；并评估了使用残余肌电图活动来解码运动意图的情况。
我们观察到不同 BMI 设计对获得的性能有显著影响。我们的结果表明，使用双侧β活动、共平均参考和自适应支持向量机可获得最佳分类结果。此外，基于大脑活动的解码结果明显高于基于肌肉活动的解码结果。
本文强调了在严重瘫痪的中风患者中使用 EEG 解码运动的不同参数的相关性。我们证明了不同设计之间的性能存在显著差异，这支持了进一步的研究，这些研究应阐明那些导致更高准确性的方法是否也能诱导瘫痪中风患者的更高运动恢复。
