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Quality parameters for a multimodal EEG/EMG/kinematic brain-computer interface (BCI) aiming to suppress neurological tremor in upper limbs

机译:旨在抑制上肢神经震颤的多模式EEG / EMG /运动脑机接口(BCI)的质量参数

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摘要

Tremor is the most common movement disorder encountered during daily neurological practice. Tremor in the upper limbs causes functional disability and social inconvenience, impairing daily life activities. The response of tremor to pharmacotherapy is variable. Therefore, a combination of drugs is often required. Surgery is considered when the response to medications is not sufficient. However, about one third of patients are refractory to current treatments. New bioengineering therapies are emerging as possible alternatives. Our study was carried out in the framework of the European project “Tremor” (ICT-2007-224051). The main purpose of this challenging project was to develop and validate a new treatment for upper limb tremor based on the combination of functional electrical stimulation (FES; which has been shown to reduce upper limb tremor) with a brain-computer interface (BCI). A BCI-driven detection of voluntary movement is used to trigger FES in a closed-loop approach. Neurological tremor is detected using a matrix of EMG electrodes and inertial sensors embedded in a wearable textile. The identification of the intentionality of movement is a critical aspect to optimize this complex system. We propose a multimodal detection of the intentionality of movement by fusing signals from EEG, EMG and kinematic sensors (gyroscopes and accelerometry). Parameters of prediction of movement are extracted in order to provide global prediction plots and trigger FES properly. In particular, quality parameters (QPs) for the EEG signals, corticomuscular coherence and event-related desynchronization/synchronization (ERD/ERS) parameters are combined in an original algorithm which takes into account the refractoriness/responsiveness of tremor. A simulation study of the relationship between the threshold of ERD/ERS of artificial EEG traces and the QPs is also provided. Very interestingly, values of QPs were much greater than those obtained for the corticomuscular module alone.
机译:震颤是在日常神经科实践中遇到的最常见的运动障碍。上肢震颤会导致功能障碍和社交不便,损害日常生活活动。震颤对药物治疗的反应是可变的。因此,经常需要药物的组合。当对药物的反应不充分时,考虑手术治疗。但是,大约三分之一的患者对目前的治疗方法无能为力。新的生物工程疗法正在出现,可能成为替代方案。我们的研究是在欧洲项目“ Tremor”(ICT-2007-224051)的框架内进行的。这个具有挑战性的项目的主要目的是基于功能性电刺激(FES;已证明可减少上肢震颤)与脑机接口(BCI)的结合,开发并验证一种新的上肢震颤治疗方法。 BCI驱动的自愿运动检测可用于以闭环方式触发FES。使用嵌入在可穿戴纺织品中的EMG电极矩阵和惯性传感器检测神经性震颤。识别运动的意图是优化此复杂系统的关键方面。我们提议通过融合来自EEG,EMG和运动传感器(陀螺仪和加速度计)的信号,对运动的意图进行多模式检测。提取运动预测参数以提供全局预测图并正确触发FES。尤其是,EEG信号的质量参数(QP),皮层相干性和与事件相关的失步/同步(ERD / ERS)参数在原始算法中组合在一起,该算法考虑了震颤的难治性/反应性。还提供了对人工EEG痕迹的ERD / ERS阈值与QP之间关系的模拟研究。非常有趣的是,QPs的值比单独使用皮质肾小球模块获得的值要大得多。

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