Gabinete de Tecnología Médica, Facultad de Ingeniería, Universidad Nacional de San Juan, San Juan, Argentina.
J Neuroeng Rehabil. 2011 Jul 14;8:39. doi: 10.1186/1743-0003-8-39.
Steady-State Visual Evoked Potential (SSVEP) is a visual cortical response evoked by repetitive stimuli with a light source flickering at frequencies above 4 Hz and could be classified into three ranges: low (up to 12 Hz), medium (12-30) and high frequency (> 30 Hz). SSVEP-based Brain-Computer Interfaces (BCI) are principally focused on the low and medium range of frequencies whereas there are only a few projects in the high-frequency range. However, they only evaluate the performance of different methods to extract SSVEP.
This research proposed a high-frequency SSVEP-based asynchronous BCI in order to control the navigation of a mobile object on the screen through a scenario and to reach its final destination. This could help impaired people to navigate a robotic wheelchair. There were three different scenarios with different difficulty levels (easy, medium and difficult). The signal processing method is based on Fourier transform and three EEG measurement channels.
The research obtained accuracies ranging in classification from 65% to 100% with Information Transfer Rate varying from 9.4 to 45 bits/min.
Our proposed method allows all subjects participating in the study to control the mobile object and to reach a final target without prior training.
稳态视觉诱发电位(SSVEP)是由重复刺激光源闪烁引起的视觉皮层反应,频率高于 4 Hz,可以分为三个范围:低频(高达 12 Hz)、中频(12-30 Hz)和高频(>30 Hz)。基于 SSVEP 的脑机接口(BCI)主要集中在低频和中频范围内,而高频范围内只有少数几个项目。然而,它们只评估了不同方法提取 SSVEP 的性能。
本研究提出了一种基于高频 SSVEP 的异步 BCI,以便通过场景控制屏幕上移动对象的导航,并到达其最终目的地。这可以帮助残障人士控制机器人轮椅。有三个不同难度级别(简单、中等和困难)的场景。信号处理方法基于傅里叶变换和三个 EEG 测量通道。
研究获得了分类精度在 65%到 100%之间的结果,信息传输率在 9.4 到 45 位/分钟之间变化。
我们提出的方法允许所有参与研究的受试者在没有预先训练的情况下控制移动对象并到达最终目标。