单通道脑电信号中诱发电位的单次提取

Single-trial Estimation of Visual Evoked Potentials in Single Channel

我们正在构建一个脑控拼写装置,直接利用脑信号与计算机进行交互。在这种实时通信模式中,作为通信载体的特征脑信号淹没在自发的脑电背景中,不能采用常规的叠加平均方法来提取,而必须实现特征信号的单次识别。为使这种技术走出实验室,记录脑电的导联数应越少越好。我们采用独特的“模拟自然阅读”诱发模式,让被试从平滑移过小视窗的刺激符号串中识别靶刺激符号,以产生视觉诱发电位(VEP)。利用支持向量机方法,对三名被试位于导联P z处EEG信号中的VEP进行了单次提取,正确识别率分别为92.1%、94.1%和91.5%,达到了较为满意的效果,为系统的实现打下了基础。

We constructed a Brain-computer interface-based mental speller which realizes user-computer interaction. The feature signals of user＇s intention are embedded in spontaneous EEG background. Single-trial feature estimation should be used on this online occasion instead of the grand average usually used in cognitive or clinical experiments. To demonstrate this technique beyond laboratories, fewer EEG recording channels are preferred. A unique paradigm, which is called imitating-natural-reading, was exploited to induce visual evoked potentials. We explored the single-trial estimation of VEP recorded in single channel using support vector machine on three subjects, and obtained satisfactory data, the classification accuracy being 92.1%, 94.1% and 91.5%, respectively. These results put forward a significant step fowards the ultimate realization of our mental speller.