摘要
针对少导联P300单次提取识别率较低的问题,提出了一种基于矩阵灰建模的参数模型法提取特征的方法,提高了P300单次识别率.首先对脑电信号进行预处理,然后选择导联组合,接着对每个Epoch进行建模,将模型参数作为特征向量输入SVM分类识别.结果表明,单次P300的平均识别率为91.43%,叠加平均3次正确率可高达97.87%.
Aiming at the drawback of lowidentification accuracy in single trial P300 feature extraction and classification,a parameter model method based on Matrix Grey Modeling to extract P300 feature was proposed to improve the recognition accuracy of the visual evoked potential P300 in single trial classification. Firstly,EEG signal was preprocessed,and then channel set selection was applied. After that,the model parameters of Matrix Grey Modelling for each epoch was connected as the feature vector and were input to the SVMclassifier. The experimental results showthat the average accuracy of single trial P300 across all the subjects is 91. 43%,and the accuracy can be up to 97. 87% if 3 times averaging is used.
出处
《电子学报》
EI
CAS
CSCD
北大核心
2017年第7期1660-1667,共8页
Acta Electronica Sinica
基金
国家自然科学基金(No.61273250)
陕西省科技攻关项目(No.2015GY003)
校研究生创业种子基金(No.Z2015112)
关键词
P300特征提取
矩阵灰建模
单次识别
P300 feature extraction
matrix grey modeling
single trial identification
