Zhang Xin, Xu Guanghua, Zhang Xun, Wu Qingqiang
School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China.
State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, China.
Front Hum Neurosci. 2018 Oct 15;12:377. doi: 10.3389/fnhum.2018.00377. eCollection 2018.
The steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) usually has the advantages of high information transfer rate (ITR) and no need for training. However, low frequencies, such as the human stride motion frequency, cannot easily induce SSVEP. To solve this problem, a light spot humanoid motion paradigm modulated by the change of brightness was designed in this study. The characteristics of the brain response to the motion paradigm modulated by the change of brightness were analyzed for the first time. The results showed that the designed paradigm could induce not only the high flicker frequency but also the modulation frequencies between the change of brightness and the motion in the primary visual cortex. Thus, the stride motion frequency can be recognized through the modulation frequencies by using the designed paradigm. Also, in an online experiment, this paradigm was employed to control a lower limb robot to achieve same frequency stimulation, which meant that the visual stimulation frequency was the same as the motion frequency of the robot. Also, canonical correlation analysis (CCA) was used to distinguish three different stride motion frequencies. The average accuracies of the classification in three walking speeds using the designed paradigm with the same and different high frequencies reached 87 and 95% respectively. Furthermore, the angles of the knee joint of the robot were obtained to demonstrate the feasibility of the electroencephalograph (EEG)-driven robot with same stimulation.
基于稳态视觉诱发电位(SSVEP)的脑机接口(BCI)通常具有信息传输率高和无需训练的优点。然而,低频信号,如人类步幅运动频率,不易诱发SSVEP。为了解决这个问题,本研究设计了一种受亮度变化调制的光点人形运动范式。首次分析了大脑对受亮度变化调制的运动范式的反应特征。结果表明,所设计的范式不仅能诱发光点的高频闪烁,还能在初级视觉皮层诱发出亮度变化与运动之间的调制频率。因此,利用所设计的范式,可通过调制频率识别步幅运动频率。此外,在在线实验中,采用该范式控制下肢机器人以实现同频刺激,即视觉刺激频率与机器人运动频率相同。同时,使用典型相关分析(CCA)区分三种不同的步幅运动频率。在相同和不同高频条件下,使用所设计范式对三种步行速度进行分类的平均准确率分别达到了87%和95%。此外,还获取了机器人膝关节的角度,以证明脑电图(EEG)驱动的同频刺激机器人的可行性。