Stawicki Piotr, Volosyak Ivan
Faculty of Technology and Bionics, Rhine-Waal University of Applied Sciences, 47533 Kleve, Germany.
Brain Sci. 2020 Sep 28;10(10):686. doi: 10.3390/brainsci10100686.
Motion-based visual evoked potentials (mVEP) is a new emerging trend in the field of steady-state visual evoked potentials (SSVEP)-based brain-computer interfaces (BCI). In this paper, we introduce different movement-based stimulus patterns (steady-state motion visual evoked potentials-SSMVEP), without employing the typical flickering. The tested movement patterns for the visual stimuli included a pendulum-like movement, a flipping illusion, a checkerboard pulsation, checkerboard inverse arc pulsations, and reverse arc rotations, all with a spelling task consisting of 18 trials. In an online experiment with nine participants, the movement-based BCI systems were evaluated with an online four-target BCI-speller, in which each letter may be selected in three steps (three trials). For classification, the minimum energy combination and a filter bank approach were used. The following frequencies were utilized: 7.06 Hz, 7.50 Hz, 8.00 Hz, and 8.57 Hz, reaching an average accuracy between 97.22% and 100% and an average information transfer rate (ITR) between 15.42 bits/min and 33.92 bits/min. All participants successfully used the SSMVEP-based speller with all types of stimulation pattern. The most successful SSMVEP stimulus was the SSMVEP1 (pendulum-like movement), with the average results reaching 100% accuracy and 33.92 bits/min for the ITR.
基于运动的视觉诱发电位(mVEP)是基于稳态视觉诱发电位(SSVEP)的脑机接口(BCI)领域中一种新出现的趋势。在本文中,我们介绍了不同的基于运动的刺激模式(稳态运动视觉诱发电位 - SSMVEP),而不采用典型的闪烁刺激。用于视觉刺激的测试运动模式包括钟摆样运动、翻转错觉、棋盘格脉动、棋盘格反弧脉动和反弧旋转,所有这些都带有一个由18次试验组成的拼写任务。在一项针对9名参与者的在线实验中,基于运动的BCI系统通过在线四目标BCI拼写器进行评估,其中每个字母可分三步(三次试验)选择。对于分类,使用了最小能量组合和滤波器组方法。使用了以下频率:7.06 Hz、7.50 Hz、8.00 Hz和8.57 Hz,平均准确率在97.22%至100%之间,平均信息传输率(ITR)在15.42比特/分钟至33.92比特/分钟之间。所有参与者都成功地使用了基于SSMVEP的拼写器与所有类型的刺激模式。最成功的SSMVEP刺激是SSMVEP1(钟摆样运动),平均结果准确率达到100%,ITR为33.92比特/分钟。