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)
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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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關(guān)鍵詞:
- 腦功能網(wǎng)絡(luò) /
- LASSO-Granger /
- 感興趣腦區(qū) /
- 特征提取
Abstract: Brain functional network is introduced to feature extraction of ElectroEncephaloGraphy (EEG), and a novel method is proposed based on Least Absolute Shrinkage and Selection Operator (LASSO)-Granger causality between Region Of Interest (ROI) in the brain, in order to overcome the inherent deficiencies of research methods based on isolated brain region. Firstly, the maximum principal component of ROIs is extracted by Principal Component Analysis (PCA), and then causality values between ROIs are calculated by LASSO-Granger. Finally, the values are used as the input vector for Support Vector Machine (SVM), and then four datasets of BCI Competition IV Dataset 1 are used for classification．Experimental results show that different motor imagery tasks are successfully identified by the method of SVM classifier combined with feature extraction which is based on LASSO-Granger causality between the brain region of interest (ROIs). This method provides a new idea for the study of extracting EEG features.-
Key words:
- Brain functional network /
- LASSO-Granger /
- Region Of Interest (ROI) /
- Feature extraction
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