摘要
以“模拟人类自然阅读诱发模式”产生的诱发脑电信号作为载体,利用脑-机接口这种新颖的人-机交互方式构建一种脑控拼写装置.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相干平均方法来提取诱发电位,而必须实现特征信号的单次识别.对来自四个通道的各种信号成分进行特征组合,利用支持向量机分类器对一名被试者脑电信号中的载波成分进行了单次提取,最佳特征组合的平均正确识别率为98.8%,证明了诱发模式的先进性和系统实现的可行性.
Exploiting potentials induced by ＂imitating-human-natural-reading inducing paradigm＂ as communication carriers, a mental speller based on brain-computer interface was constructed. In this online paradigm, single-trial feature estimation should be used instead of grand average that usually used in the cognitive or clinical experiments. With several montages of component features from 4 channels, and with carefully parameters optimizing, the single-trial estimation of evoked potentials using support vector machines in a subject was explored, and gained perfect classification rate of 98.8 %. The results show the advantages of the inducing paradigm used in our mental speller.
出处
《华中科技大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2006年第8期19-22,共4页
Journal of Huazhong University of Science and Technology(Natural Science Edition)
基金
国家自然科学基金资助项目(30370393)
中南民族大学引进人才启动基金资助项目(YZZ05015)
关键词
单次提取
脑-机接口
特征选择
诱发电位
支持向量机
single-trial estimation
brain-computer interface （BCI）
feature selection
visual-evoked potentials
support vector machine （SVM）.
