Grimaldi Giuliana, Manto Mario, Jdaoudi Yassin
Unité d'Etude du Mouvement, Université Libre de Bruxelles, Erasme, Bruxelles, 1070, Belgium.
Unité d'Etude du Mouvement, Université Libre de Bruxelles, Erasme, Bruxelles, 1070, Belgium ; Fonds de la Recherche Scientifique, Université Libre de Bruxelles, Bruxelles, 1070, Belgium.
F1000Res. 2013 Dec 20;2:282. doi: 10.12688/f1000research.2-282.v2. eCollection 2013.
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.
震颤是日常神经科诊疗中最常见的运动障碍。上肢震颤会导致功能残疾和社交不便，影响日常生活活动。震颤对药物治疗的反应因人而异。因此，通常需要联合用药。当药物治疗效果不佳时，会考虑手术治疗。然而，约三分之一的患者对目前的治疗方法无效。新的生物工程疗法正在成为可能的替代方案。我们的研究是在欧洲“震颤”项目（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开发并验证一种基于功能性电刺激（FES，已证明可减轻上肢震颤）与脑机接口（BCI）相结合的上肢震颤新疗法。利用脑机接口驱动的自主运动检测，以闭环方式触发功能性电刺激。使用嵌入可穿戴纺织品中的肌电图电极矩阵和惯性传感器来检测神经源性震颤。运动意向的识别是优化这个复杂系统的关键环节。我们提出通过融合脑电图（EEG）、肌电图（EMG）和运动传感器（陀螺仪和加速度计）的信号，对运动意向进行多模态检测。提取运动预测参数，以提供全局预测图并正确触发功能性电刺激。特别是，将脑电图信号的质量参数（QPs）、皮质 - 肌肉连贯性以及事件相关去同步化/同步化（ERD/ERS）参数组合在一种原始算法中，该算法考虑了震颤的难治性/反应性。还提供了关于人工脑电图轨迹的ERD/ERS阈值与质量参数之间关系的模拟研究。非常有趣的是，质量参数的值比仅使用皮质 - 肌肉模块时获得的值要大得多。
