Abstract: Exploiting the novel human-machine interaction paradigm called brain–computer interface, we constructed a mental speller for those with neuromuscular disorders and motor disabilities. The carriers of communication were induced by a so-called “imitating natural reading” paradigm. Single-trial feature estimating should be used in this online paradigm instead of grand average that usually used in the cognitive or clinical experiments. we explored the single-trial estimation of P300 from channel Pz using support vector machine in three subjects, and gained a satisfied classification accuracy, which is 91.3%, 88.9% and 91.5% respectively. These results demonstrated the advantages of the inducing paradigm in constructing our mental speller.

摘要： 利用脑－机接口这种新颖的人－机交互模式构建一种脑控拼写器，用于帮助高位截瘫患者实现与外界的交流。该系统采用模拟人类自然阅读的诱发模式，大脑自主地进行选择性输入。在这种实时通信模式中，不能采用认知科学实验及临床中的常规叠加平均方法来提取VEP，而必须实现特征信号的单次识别。本文利用支持向量机分类器对三名被试的脑电信号的载波成分进行了单次提取，特征数据来自通道Pz，以300ms~600ms时段的P300成分作为特征信号，对靶刺激的正确识别率分别为91.3%、88.9%和91.5%，证明了诱发模式的先进性，为系统的实现打下了基础。