Gudiño-Mendoza Berenice, Sanchez-Ante Gildardo, Antelis Javier M
Tecnologico de Monterrey, Campus Guadalajara, Avenida General Ramón Corona 2514, 45201 Zapopan, JAL, Mexico.
Comput Math Methods Med. 2016;2016:3195373. doi: 10.1155/2016/3195373. Epub 2016 Apr 27.
Early decoding of motor states directly from the brain activity is essential to develop brain-machine interfaces (BMI) for natural motor control of neuroprosthetic devices. Hence, this study aimed to investigate the detection of movement information before the actual movement occurs. This information piece could be useful to provide early control signals to drive BMI-based rehabilitation and motor assisted devices, thus providing a natural and active rehabilitation therapy. In this work, electroencephalographic (EEG) brain signals from six healthy right-handed participants were recorded during self-initiated reaching movements of the upper limbs. The analysis of these EEG traces showed that significant event-related desynchronization is present before and during the execution of the movements, predominantly in the motor-related α and β frequency bands and in electrodes placed above the motor cortex. This oscillatory brain activity was used to continuously detect the intention to move the limbs, that is, to identify the motor phase prior to the actual execution of the reaching movement. The results showed, first, significant classification between relax and movement intention and, second, significant detection of movement intention prior to the onset of the executed movement. On the basis of these results, detection of movement intention could be used in BMI settings to reduce the gap between mental motor processes and the actual movement performed by an assisted or rehabilitation robotic device.
直接从大脑活动中早期解码运动状态对于开发用于神经假体装置自然运动控制的脑机接口（BMI）至关重要。因此，本研究旨在调查在实际运动发生之前对运动信息的检测。这条信息对于提供早期控制信号以驱动基于BMI的康复和运动辅助装置可能有用，从而提供一种自然且主动的康复治疗。在这项工作中，在六名健康右利手参与者进行上肢自主伸展运动期间记录了脑电图（EEG）脑信号。对这些EEG轨迹的分析表明，在运动执行之前和期间存在显著的事件相关去同步化，主要出现在与运动相关的α和β频段以及位于运动皮层上方的电极处。这种振荡性脑活动被用于持续检测肢体运动意图，即识别在实际执行伸展运动之前的运动阶段。结果显示，第一，在放松和运动意图之间有显著分类，第二，在执行运动开始之前对运动意图有显著检测。基于这些结果，运动意图检测可用于BMI设置，以缩小心理运动过程与辅助或康复机器人装置执行的实际运动之间的差距。
