• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

计算机工程与科学

• 论文 • 上一篇    下一篇

基于独立成分分析的优选N200和P300特征通道算法

李文轩1,李伟2,李梦凡1,刘成用1   

  1. (1.天津大学电气与自动化工程学院,天津 300072;
    2.加州州立大学贝克斯菲尔德分校计算机系&电气工程与计算机科学学院,加利福尼亚 贝克尔斯菲德市 93311)
     
  • 收稿日期:2016-01-04 修回日期:2016-04-12 出版日期:2017-09-25 发布日期:2017-09-25
  • 基金资助:

    国家自然科学基金(61473207)

Independent component analysis-based channel selection
for high-accuracy classification of N200 and P300

LI Wen-xuan1,LI Wei2,LI Meng-fan1,LIU Cheng-yong1   

  1. (1.School of Electrical Engineering and Automation,Tianjin University,Tianjin 300072,China;
    2.Department of Computer & Electrical Engineering and Computer Science,
    California State University,Bakersfield  93311,USA)

     
  • Received:2016-01-04 Revised:2016-04-12 Online:2017-09-25 Published:2017-09-25

摘要:

针对脑电信号存在个体差异性并易受噪声、伪迹干扰的特点,提出一种基于独立成分分析ICA的优选特征通道算法。采用ICA将通道的数据分解为N200、P300、眼电伪迹以及其他生理信号,根据这些信号对每个通道的影响程度,判定各通道是否适合进行特征提取。分别采用本方法和三种常用方法对12个被试的脑电数据进行特征通道选择,并进行N200和P300电位的辨识,经比对发现,本文方法取得了93.10%的平均分类准确率,比其他三种方法下的准确率分别高出7.27%、1.07%和75.96%。为预测任意被试的最优通道,采用最小二乘法对ICA权值和通道选择阈值之间的关系进行拟合,对三个新被试进行最优通道预测和电位的辨识,得到较高的分类准确率,说明此预测方法具有一定普适性。

 

关键词: 通道选择, 独立成分分析, 个体差异, 伪迹, 分类准确率

Abstract:

Since EEG signals have individual difference and are vulnerable to noise and artifacts, we propose an independent component analysis (ICA)-based method for the selection of optimal feature channels. This method applies the ICA to decompose channels’ data to N200, P300, ocular artifacts and other physiological signals. Whether a channel is suitable for feature extraction is decided by the influence of those signals that mentioned above to this channel. We apply our method and three other commonly used methods for feature channel selection to twelve subjects’ brain signals, and recognize N200 and P300 potentials. We find that our method achieves a 93.10% accuracy on average and it is 7.27%, 1.07% and 75.96% higher than the average accuracy of the other three methods respectively. We fit a relation curve between ICA weight and channel selection threshold based on the least square method, and obtain a high classification accuracy when predicting the optimal channels and recognizing the potentials from another three new subjects' data, which means that this prediction method has universality.

Key words: channel selection, independent component analysis(ICA), individual difference, artifact, classification accuracy