Department of Biomedical Engineering, College of Medicine, Keimyung University, South Korea.
Department of Biomedical Engineering, College of Medicine, Keimyung University, South Korea.
J Neurosci Methods. 2019 Feb 15;314:21-27. doi: 10.1016/j.jneumeth.2019.01.005. Epub 2019 Jan 16.
The number of commands in a brain-computer interface (BCI) system is important. This study proposes a new BCI technique to increase the number of commands in a single BCI system without loss of accuracy.
We expected that a flickering action video with left and right elbow movements could simultaneously activate the different pattern of event-related desynchronization (ERD) according to the video contents (e.g., left or right) and steady-state visually evoked potential (SSVEP). The classification accuracy to discriminate left, right, and rest states was compared under the three following feature combinations: SSVEP power (19-21 Hz), Mu power (8-13 Hz), and simultaneous SSVEP and Mu power.
The SSVEP feature could discriminate the stimulus condition, regardless of left or right, from the rest condition, while the Mu feature discriminated left or right, but was relatively poor in discriminating stimulus from rest. However, combining the SSVEP and Mu features, which were evoked by the stimulus with a single frequency, showed superior performance for discriminating all the stimuli among rest, left, or right.
The video contents could activate the ERD differently, and the flickering component increased its accuracy, such that it revealed a better performance to discriminate when considering together.
This paradigm showed possibility of increasing performance in terms of accuracy and number of commands with a single frequency by applying flickering action video paradigm and applicability to rehabilitation systems used by patients to facilitate their mirror neuron systems while training.
脑机接口(BCI)系统中的命令数量很重要。本研究提出了一种新的 BCI 技术,可在不降低准确性的情况下增加单个 BCI 系统中的命令数量。
我们期望具有左右肘运动的闪烁动作视频可以根据视频内容(例如,左或右)和稳态视觉诱发电位(SSVEP)同时激活不同的事件相关去同步(ERD)模式。比较了以下三种特征组合下的左、右和休息状态的分类准确性:SSVEP 功率(19-21 Hz)、Mu 功率(8-13 Hz)和同时 SSVEP 和 Mu 功率。
SSVEP 特征可区分刺激条件与休息条件,而 Mu 特征可区分左或右,但在区分刺激与休息方面相对较差。然而,同时结合由单个频率刺激引起的 SSVEP 和 Mu 特征,在区分休息、左或右的所有刺激方面表现出更好的性能。
视频内容可以以不同的方式激活 ERD,闪烁成分提高了其准确性,因此考虑到这一点,它显示出更好的区分性能。
该范式通过应用闪烁动作视频范式和适用于帮助患者训练镜像神经元系统的康复系统,显示出通过应用单一频率提高准确性和命令数量的可能性。