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Improving recognition rate of motor imagery (MI)-related electroencephalography (EEG) is of great importance for numerous brain computer interface (BCI) applications. However, the performance of a typical BCI system greatly relies on the effectiveness of the extracted features from raw EEG signals and the ability of the classifier to correctly identify different MI patterns. Therefore, in this paper, a new recognition method based on adaptive parameterless empirical wavelet transform (APEWT) and selective integrated classification model is proposed to enhance the classification accuracy of MI-related EEG signal. First, the APEWT is used to decompose EEG signals from different MI patterns into several intrinsic mode functions (IMFs), each of which contains different rhythm information over different frequency bands. Then several related modes are optimally selected based on the correlation coefficients calculated between each IMF component and the original signal to reconstruct EEG signals. Next, in order to further extract useful pattern information from both the time domain and frequency domain, the energy spectrum features of multiple time segments from the reconstructed signals and marginal spectrum features of different frequency bands corresponding to those selected modes are investigated, respectively. Finally, the extracted multiple features from time domain and frequency domain are input into the selective integrated classification model to build an MI recognition system. The selective integrated classification model consists of several extreme learning machines (ELMs) as the basic classifiers, different weights are assigned, respectively, to ELM basic classifiers based on their corresponding classification performances, and several basic ELM classifiers with good performances are selected to construct the final integrated model. The proposed method is evaluated on two public datasets, including BCI Competition Ⅱ dataset Ⅲ and BCI Competition IV dataset 2 b, and is compared with four different combination methods where different features in time domain or frequency domain in the feature extraction stage and different ELMs based classification models are considered. Experimental results demonstrate that the proposed method outperformed four combination methods and the existing methods recently reported in the literature using the same datasets in terms of classification accuracy and area under the ROC curve receiver operating characteristic metric. Specifically, our proposed method achieves the highest average classification accuracy (89.95%) in the compared methods, which indicates its better classification performance and generalization capability. In addition, the proposed method exhibits high computational efficiency, thus providing a new solution for online recognition of MI-related BCI and having the potential to be embedded in a practical system for controlling an external device.
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Keywords:
- adaptive parameterless empirical wavelet transform /
- selective integrated classification model /
- motor imagery /
- brain-computer interface
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[19] Hou B W 2012 M. S. Dissertation (Xi'an: Xidian University) (in Chinese) 侯秉文 2012 硕士学位论文 (西安: 西安电子科技大学)
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[26] Suk H I, Lee S W 2013 Trans. Patt. Anal. Mach. Intelli 35 286
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[1] Zhang D, Li J W 2017 Sci. Technol. Rev. 35 62(in Chinese) [张丹, 李佳蔚 2017 科技导报 35 62]
[2] Gao S, Wang Y, Gao X, Hong B 2014 Trans. Biomed. Eng. 61 1436
[3] Abdulkader S N, Atia A, Mostafa M S M 2015 Egy. Inf. J. 16 213
[4] Fang X L, Jiang Z L 2007 Acta Phys. Sin. 56 7330(in Chinese) [方小玲, 姜宗来 2007 物理学报 56 7330]
[5] Pfurtscheller G, da Silva F H L 1999 Cli. Neurophysiol. 110 1842
[6] Lei M, Meng G, Zhang W M, Nilanjan S 2016 Acta Phys. Sin. 65 108701(in Chinese) [雷敏, 孟光, 张文明, Nilanjan Sarkar 2016 物理学报 65 108701]
[7] Lei M, Meng G, Zhang W M, Joshua W, Nilanjan S 2016 Entropy 18 412
[8] Wang Y, Hou F Z, Dai J F, Liu X F, Li J, Wang J 2014 Acta Phys. Sin. 63 218701(in Chinese) [王莹, 侯凤贞, 戴加飞, 刘新峰, 李锦, 王俊 2014 物理学报 63 218701]
[9] Xu B, Song A 2008 J. Biomed. Sci. Eng. 1 64
[10] Sun H W, Fu Y F, Xiong X, Yang J, Liu C W, Yu Z T 2015 Acta Automatica Sin. 41 1686(in Chinese) [孙会文, 伏云发, 熊馨, 杨俊, 刘传伟, 余正涛 2015 自动化学报 41 1686]
[11] Xie P, Chen X L, Su Y P, Liang Z H, Li X L 2013 Chin. J. Biomed. Eng. 32 641(in Chinese) [谢平, 陈晓玲, 苏玉萍, 梁振虎, 李小俚 2013 中国生物医学工程学报 32 641]
[12] Yang M H, Chen W Z, Li M Y 2017 Acta Automatica Sin. 43 743(in Chinese) [杨默涵, 陈万忠, 李明阳 2017 自动化学报 43 743]
[13] Wu L Y, Lu H, Gao N, Wang T 2017 Chin. J. Biomed. Eng. 36 224(in Chinese) [吴林彦, 鲁昊, 高诺, 王涛 2017 生物医学工程研究 36 224]
[14] Zhang X Q, Liang J 2013 Acta Phys. Sin. 62 050505(in Chinese) [张学清, 梁军 2013 物理学报 62 050505]
[15] Tang Z C, Sun S Q, Zhang K J 2017 Chin. J. Mech. Eng. 53 60(in Chinese) [唐智川, 孙守迁, 张克俊 2017 机械工程学报 53 60]
[16] Gilles J 2013 IEEE Trans. Signal Proc. 61 3999
[17] Gilles J, Heal K 2014 Multiresol. Inf. Proces 12 1450044
[18] Zheng J, Pan H, Yang S, Cheng J 2017 Signal. Proces 130 305
[19] Hou B W 2012 M. S. Dissertation (Xi'an: Xidian University) (in Chinese) 侯秉文 2012 硕士学位论文 (西安: 西安电子科技大学)
[20] Zhao Y, Chen R, Liu W 2016 Comput. Sci. 8 177(in Chinese) [赵宇, 陈锐, 刘蔚 2016计算机科学 8 177]
[21] Lindeberg T, ter Haar R B M 1994 Linear Scale-Space I: Basic Theory (Netherlands: Springer) pp1-38
[22] Cai Y P, Li A H, Wang T, Yao L, Xu P 2010 J. Vibra. Eng. 4 430(in Chinese) [蔡艳平, 李艾华, 王涛, 姚良, 许平 2010 振动工程学报 4 430]
[23] Huang G B, Zhu Q Y, Siew C K 2004 IEEE International Joint Conference on Neural Networks Proceedings Budapest, Hungary, July 25-29, 2004 p985
[24] Huang G B, Zhu Q Y, Siew C K 2006 Neurocomput 70 489
[25] Bentlemsan M, Zemouri E T T, Bouchaffra D, Yahya-Zoubir, B, Ferroudji, K 2014 5th International Conference on Intelligent Systems, Modelling and Simulation, Langkawi, Malaysia, Jan. 27-29, 2014 p235
[26] Suk H I, Lee S W 2013 Trans. Patt. Anal. Mach. Intelli 35 286
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