基于多个伪随机序列调制的视觉脑机接口研究

传统的基于编码调制的视觉脑机接口(BCI)使用一种编码及其移位来调制不同的视觉目标,限制了目标数的增加,因而限制了系统的信息传输率。基于两种不同类型的三个伪随机序列调制实现了一个48个刺激目标的BCI系统。采用两个不同的Golay码和一个近完美序列对目标进行调制,使得每个码的自相关特性和每个码之间的互相关特性都很好,大大提高了刺激目标数。通过模板匹配法对训练数据进行分类识别获得了很高的识别准确率,并和一类支持向量机方法进行了比较。选取了8个受试者进行了实验膜板匹配法的平均识别准确率达到93.49%,证明了这是一种提高刺激目标数的好方法。

Study on visual brain-computer interfaces based on modulation of multiple pseudo-random sequences

Traditional brain-computer interface (BCI) systems based on code modulation use only one kind of code and its shifts to modulate different visual targets, limiting the increase of the number of targets and thus the information transfer rate (ITR) of the system. Based on the modulation of three pseudo-random sequences of two kinds, a 48-target BCI system was implemented. Two different Golay codes and an almost perfect sequence are used for target modulation, resulting in good autocorrelation and cross-correlation properties between any two modulation codes and a substantial increase of the number of stimulus targets. The training data are classified and identified by the template matching method(TMM), and a high recognition accuracy is obtained, which is compared with one class support vector machine(OCSVM). Eight subjects were selected for experiment and the average recognition accuracy of the template matching method was 93.49%, which proved that it was a good method to increase the number of stimulus targets.