Rocon E, Gallego J A, Barrios L, Victoria A R, Ibanez J, Farina D, Negro F, Dideriksen J L, Conforto S, D'Alessio T, Severini G, Belda-Lois J M, Popovic L Z, Grimaldi G, Manto M, Pons J L
Bioengineering Group, CSIC, Madrid, Spain.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:3337-40. doi: 10.1109/IEMBS.2010.5627914.
Tremor constitutes the most common movement disorder; in fact 14.5% of population between 50 to 89 years old suffers from it. Moreover, 65% of patients with upper limb tremor report disability when performing their activities of daily living (ADL). Unfortunately, 25% of patients do not respond to drugs or neurosurgery. In this regard, TREMOR project proposes functional compensation of upper limb tremors with a soft wearable robot that applies biomechanical loads through functional electrical stimulation (FES) of muscles. This wearable robot is driven by a Brain Neural Computer Interface (BNCI). This paper presents a multimodal BCI to assess generation, transmission and execution of both volitional and tremorous movements based on electroencephalography (EEG), electromyography (EMG) and inertial sensors (IMUs). These signals are combined to obtain: 1) the intention to perform a voluntary movement from cortical activity (EEG), 2) tremor onset, and an estimation of tremor frequency from muscle activation (EMG), and 3) instantaneous tremor amplitude and frequency from kinematic measurements (IMUs). Integration of this information will provide control signals to drive the FES-based wearable robot.
震颤是最常见的运动障碍；事实上，50至89岁的人群中有14.5%患有震颤。此外，65%的上肢震颤患者在进行日常生活活动（ADL）时报告有残疾。不幸的是，25%的患者对药物或神经外科手术无反应。在这方面，TREMOR项目提出用一种软可穿戴机器人对上肢震颤进行功能补偿，该机器人通过对肌肉的功能性电刺激（FES）施加生物力学负荷。这种可穿戴机器人由脑神经网络计算机接口（BNCI）驱动。本文提出了一种多模态脑机接口，用于基于脑电图（EEG）、肌电图（EMG）和惯性传感器（IMU）评估自主运动和震颤运动的产生、传递和执行。这些信号被组合起来以获得：1）从皮层活动（EEG）中执行自主运动的意图，2）震颤发作，以及从肌肉激活（EMG）中估计震颤频率，3）从运动学测量（IMU）中获取瞬时震颤幅度和频率。这些信息的整合将提供控制信号来驱动基于FES的可穿戴机器人。
