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基于感興趣腦區(qū)LASSO-Granger因果關(guān)系的腦電特征提取算法

佘青山 陳希豪 高發(fā)榮 羅志增

佘青山, 陳希豪, 高發(fā)榮, 羅志增. 基于感興趣腦區(qū)LASSO-Granger因果關(guān)系的腦電特征提取算法[J]. 電子與信息學(xué)報, 2016, 38(5): 1266-1270. doi: 10.11999/JEIT150851
引用本文: 佘青山, 陳希豪, 高發(fā)榮, 羅志增. 基于感興趣腦區(qū)LASSO-Granger因果關(guān)系的腦電特征提取算法[J]. 電子與信息學(xué)報, 2016, 38(5): 1266-1270. doi: 10.11999/JEIT150851
SHE Qingshan, CHEN Xihao, GAO Farong, LUO Zhizeng. Feature Extraction of Electroencephalography Based on LASSO-Granger Causality Between Brain Region of Interest[J]. Journal of Electronics & Information Technology, 2016, 38(5): 1266-1270. doi: 10.11999/JEIT150851
Citation: SHE Qingshan, CHEN Xihao, GAO Farong, LUO Zhizeng. Feature Extraction of Electroencephalography Based on LASSO-Granger Causality Between Brain Region of Interest[J]. Journal of Electronics & Information Technology, 2016, 38(5): 1266-1270. doi: 10.11999/JEIT150851

基于感興趣腦區(qū)LASSO-Granger因果關(guān)系的腦電特征提取算法

doi: 10.11999/JEIT150851
基金項(xiàng)目: 

國家自然科學(xué)基金(61201302, 61172134),國家留學(xué)基金(201308330297),浙江省自然科學(xué)基金(LY15F010009)

Feature Extraction of Electroencephalography Based on LASSO-Granger Causality Between Brain Region of Interest

Funds: 

The National Natural Science Foundation of China (61201302, 61172134), State Scholarship Fund of China (201308330297), Natural Science Foundation of Zhejiang Province (LY15F010009)

  • 摘要: 該文將腦功能網(wǎng)絡(luò)引入到腦電特征提取的研究中,提出一種基于感興趣腦區(qū)LASSO-Granger因果關(guān)系的新方法,克服了當(dāng)前基于孤立腦區(qū)的研究方法的不足。先利用主成分分析提取各感興趣區(qū)的最大主成分,然后計算它們之間的LASSO-Granger因果度量,并將其作為特征向量,最后輸入支持向量機(jī)分類器,對BCI Competition IV dataset 1中的4組數(shù)據(jù)進(jìn)行分類識別。結(jié)果表明,基于感興趣腦區(qū)間LASSO-Granger因果關(guān)系分析和支持向量機(jī)分類器的方法對不同的運(yùn)動想象任務(wù)識別率較高,提供了新的研究思路。
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出版歷程
  • 收稿日期:  2015-07-16
  • 修回日期:  2016-01-29
  • 刊出日期:  2016-05-19

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