Nakanishi Masaki, Wang Yijun, Wang Yu-Te, Mitsukura Yasue, Jung Tzyy-Ping
Graduate School of Science and Technology, Keio University, Yokohama, Kanagawa, 223-8522, Japan.
Int J Neural Syst. 2014 Sep;24(6):1450019. doi: 10.1142/S0129065714500191. Epub 2014 Jun 12.
Implementing a complex spelling program using a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) remains a challenge due to difficulties in stimulus presentation and target identification. This study aims to explore the feasibility of mixed frequency and phase coding in building a high-speed SSVEP speller with a computer monitor. A frequency and phase approximation approach was developed to eliminate the limitation of the number of targets caused by the monitor refresh rate, resulting in a speller comprising 32 flickers specified by eight frequencies (8-15 Hz with a 1 Hz interval) and four phases (0°, 90°, 180°, and 270°). A multi-channel approach incorporating Canonical Correlation Analysis (CCA) and SSVEP training data was proposed for target identification. In a simulated online experiment, at a spelling rate of 40 characters per minute, the system obtained an averaged information transfer rate (ITR) of 166.91 bits/min across 13 subjects with a maximum individual ITR of 192.26 bits/min, the highest ITR ever reported in electroencephalogram (EEG)-based BCIs. The results of this study demonstrate great potential of a high-speed SSVEP-based BCI in real-life applications.
由于刺激呈现和目标识别方面的困难,使用基于稳态视觉诱发电位(SSVEP)的脑机接口(BCI)实现复杂的拼写程序仍然是一项挑战。本研究旨在探索混合频率和相位编码在构建基于计算机显示器的高速SSVEP拼写器中的可行性。开发了一种频率和相位近似方法,以消除由显示器刷新率引起的目标数量限制,从而得到一个由八个频率(8 - 15Hz,间隔1Hz)和四个相位(0°、90°、180°和270°)指定的32个闪烁组成的拼写器。提出了一种结合典型相关分析(CCA)和SSVEP训练数据的多通道方法用于目标识别。在模拟在线实验中,以每分钟40个字符的拼写速度,该系统在13名受试者中获得了平均166.91比特/分钟的信息传输率(ITR),最高个体ITR为192.26比特/分钟,这是基于脑电图(EEG)的脑机接口中报道过的最高ITR。本研究结果证明了基于高速SSVEP的脑机接口在实际应用中的巨大潜力。