Loopez-Larraz Eduardo, Birbaumer Niels, Ramos-Murguialday Ander
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:2000-2003. doi: 10.1109/EMBC.2018.8512711.
Motor rehabilitation based on brain-machine interfaces (BMI) has been shown as a feasible option for stroke patients with complete paralysis. However, the pathologic EEG activity after a stroke makes the detection of movement intentions in these patients challenging, especially in those with damages involving the motor cortex. Residual electromyographic activity in those patients has been shown to be decodable, even in cases when the movement is not possible. Hybrid BMIs combining EEG and EMG activity have been recently proposed, although there is little evidence about how they work for completely paralyzed stroke patients. In this study we propose a neural interface, relying on EEG, EMG or EEG+EMG features, to detect movement attempts. Twenty patients with a chronic stroke affecting their motor cortex were recruited, and asked to open and close their paralyzed hand while their electrophysiological signals were recorded. We show how EEG and EMG activities provide complementary information for detecting the movement intentions, being the accuracy of the hybrid BMI significantly higher than the EEG-based system. The obtained results encourage the integration of hybrid BMI systems for motor rehabilitation of patients with paralysis due to stroke.
基于脑机接口（BMI）的运动康复已被证明是完全瘫痪中风患者的一种可行选择。然而，中风后的病理性脑电图活动使得检测这些患者的运动意图具有挑战性，尤其是在那些运动皮层受损的患者中。这些患者的残余肌电活动已被证明是可解码的，即使在无法运动的情况下也是如此。最近有人提出了结合脑电图和肌电活动的混合BMI，尽管关于它们如何对完全瘫痪的中风患者起作用的证据很少。在本研究中，我们提出了一种基于脑电图、肌电或脑电图+肌电特征的神经接口，以检测运动尝试。招募了20名患有影响其运动皮层的慢性中风患者，并要求他们在记录其电生理信号时张开和闭合瘫痪的手。我们展示了脑电图和肌电活动如何为检测运动意图提供互补信息，混合BMI的准确性明显高于基于脑电图的系统。所得结果鼓励将混合BMI系统整合用于中风后瘫痪患者的运动康复。
