Feature Combination and Correlation Analysis for Motor Imagery EEG
- DOI
- 10.2991/ifmeita-16.2016.131How to use a DOI?
- Keywords
- motor imagery EEG, Sample Entropy, orthogonal empirical mode decomposition, correlation coefficient，feature combination
- Abstract
In order to get high classification accuracy, feature combination is commonly used in analysis of motor imagery electroencephalography EEG signal, including the nonlinear analysis and traditional time-frequency analysis. In this paper, Sample entropy(SampEn) was computed and represented as the nonlinear feature of motor imagery EEG signal for it can quantify the probability of new information appeared in time series. In addition, orthogonal empirical mode decomposition (OEMD) was also employed to extract the average energy of selected intrinsic mode functions(IMF) as the time-frequency feature for motor imagery EEG signal. Based on a public dataset, many experiments were conducted. Slide window was used to select the best time period for a better performance in feature extraction, and cross validation of 10 folds was applied in all the classification procedure. The highest recognition rate using SampEn and OEMD is respectively 86.07% and 83.21% classified by incremental support vector machine (ISVM) respectively. However, the highest classification rate of combined features is 86.79% by using ISVM which is a little higher than that of SampEn. Big linear correlation between SampEn and energy of IMF explains why the classification accuracy by combining the two types of features is not as high as expected.
- Copyright
- © 2016, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Mingai Li AU - Jinfeng Xu AU - Xinyong Luo PY - 2016/01 DA - 2016/01 TI - Feature Combination and Correlation Analysis for Motor Imagery EEG BT - Proceedings of the 2016 International Forum on Management, Education and Information Technology Application PB - Atlantis Press SP - 717 EP - 721 SN - 2352-5398 UR - https://doi.org/10.2991/ifmeita-16.2016.131 DO - 10.2991/ifmeita-16.2016.131 ID - Li2016/01 ER -