A New Single Trial P300 Classification Method Based on Matrix Grey Modeling

XIE Song-yun; Zhang Juan-li; DUAN Xu; LIU Chang; LI Ya-bing

doi:10.3969/j.issn.0372-2112.2017.07.016

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A New Single Trial P300 Classification Method Based on Matrix Grey Modeling

更新时间：2025-07-16

- A New Single Trial P300 Classification Method Based on Matrix Grey Modeling
- Acta Electronica Sinica Vol. 45, Issue 7, Pages: 1660-1667(2017)
- 作者机构：
  
  西北工业大学电子信息学院,陕西,西安,710072
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61273250);Key Technology Research and Development Program of Shaanxi Province (No.2015GY003);Graduate Startup Seed Funding (No.Z2015112)
- DOI：10.3969/j.issn.0372-2112.2017.07.016
  CLC： TP391.4
- Published Online：25 July 2017，
  
  Published：2017
- 稿件说明：
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XIE Song-yun, Zhang Juan-li, DUAN Xu, et al. A New Single Trial P300 Classification Method Based on Matrix Grey Modeling[J]. Acta Electronica Sinica, 2017, 45(7): 1660-1667.
DOI：

XIE Song-yun, Zhang Juan-li, DUAN Xu, et al. A New Single Trial P300 Classification Method Based on Matrix Grey Modeling[J]. Acta Electronica Sinica, 2017, 45(7): 1660-1667. DOI： 10.3969/j.issn.0372-2112.2017.07.016.

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%.

Abstract

Aiming at the drawback of low identification 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 raise 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 SVM classifier.The experimental results show that 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.

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