Feature Extraction of Motor Imagery Electroencephalography Based on Time-frequency-space Domains_Journal of Biomedical Engineering

Authors：

WANGYueru ^1,2 ,  LIXin ^1,2,3 , LIHonghong ^1,2 , SHAOChengcheng ^1,2 , YINGLijuan ^1,2 , WUShuicai ³

1. Institute of Biomedical Engineering, Yanshan University, Qinhuangdao 066004, China;
2. Measurement Technology and Instrumentation Key Lab of Hebei Province, Qinhuangdao 066004, China;
3. The College of Life Science and Bio-Engineering, Beijing University of Technology, Beijing 100124, China;

Corresponding author：

LIXin, Email: yddylixin@ysu.edu.cn

Keywords：

electroencephalography; feature extraction; independent component analysis; wavelet transform; support vector machine

DOI：

10.7507/1001-5515.20140180

Video：

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The purpose of using brain-computer interface (BCI) is to build a bridge between brain and computer for the disable persons, in order to help them to communicate with the outside world. Electroencephalography (EEG) has low signal to noise ratio (SNR), and there exist some problems in the traditional methods for the feature extraction of EEG, such as low classification accuracy, lack of spatial information and huge amounts of features. To solve these problems, we proposed a new method based on time domain, frequency domain and space domain. In this study, independent component analysis (ICA) and wavelet transform were used to extract the temporal, spectral and spatial features from the original EEG signals, and then the extracted features were classified with the method combined support vector machine (SVM) with genetic algorithm (GA). The proposed method displayed a better classification performance, and made the mean accuracy of the Graz datasets in the BCI Competitions of 2003 reach 96%. The classification results showed that the proposed method with the three domains could effectively overcome the drawbacks of the traditional methods based solely on time-frequency domain when the EEG signals were used to describe the characteristics of the brain electrical signals.

Citation： WANGYueru, LIXin, LIHonghong, SHAOChengcheng, YINGLijuan, WUShuicai. Feature Extraction of Motor Imagery Electroencephalography Based on Time-frequency-space Domains. Journal of Biomedical Engineering, 2014, 31(5): 955-961. doi: 10.7507/1001-5515.20140180 Copy

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1. LI X, WANG Y R, SONG J L, et al. Research on classification method of combining support vector machine and genetic algorithm for motor imagery EEG[J]. J Comput Inform Syst, 2011, 7(12): 4351-4358.
2. CURRAN E A, STOKES M J. Learning to control brain activity: a review of the production and control of EEG components for driving brain-computer interface (BCI) systems[J]. Brain Cogn, 2003, 51(3):326-336.
3. XU Q, ZHOU H, WANG Y J, et al. Fuzzy support vector machine for classification of EEG signals using wavelet-based features[J]. Med Eng Phys, 2009, 31(7): 858-865.
4. DELORME A, MAKEIG S. EEG changes accompanying learned regulation of 12-Hz EEG activity[J]. IEEE Trans Neural Syst Rehabil Eng, 2003, 11(2): 133-137.
5. 高海波,崔建新,洪文学,等.用基于平行坐标图的可视化人机交互技术提取EEG信号特征的方法[J].高技术通讯,2010,20(5):518-523.
6. KEIRN Z A, AUNON J I. A new mode of communication between man and his surroundings[J]. IEEE Trans Biomed Eng, 1990, 37(12): 1209-1214.
7. PEI X M, ZHENG C X. Feature extraction and classification of brain motor imagery task based on MVAR model [C]//Proceedings of the Third International Conference on Machine Learning and Cybernetics. Shanghai: 2004: 3726-3730.
8. HAN M, SUN L L. EEG signal classification for epilepsy diagnosis based on AR model and RVM [C]//International Conference on Intelligent Control and Information Processing (ICICIP). Dalian: 2010: 134-139.
9. 徐兴,徐胜,王卫星,等.基于自适应自回归模型的非线性流量负荷预测[J].科学技术与工程,2011,11(18):4219-4222.
10. 王金甲,周丽娜,赵玉超.基于MEG的脑机接口特征提取方法研究[J].仪器仪表学报,2010,31(7):1460-1465.
11. 王志宇,王宏,李一娜,等.小波相关分析在脑-计算机接口系统中的研究[J].仪器仪表学报,2006,27(4):358-362.
12. 王攀,沈继忠,施锦河.基于小波变换和时域能量熵的P300特征提取算法[J].仪器仪表学报,2011,32(6):1284-1289.
13. 何庆华,吴宝明,彭承琳,等.基于小波和神经网络的视觉诱发电位识别方法[J].仪器仪表学报,2007,28(6):1003-1006.
14. INCE N F, GOKSU F, TEWFIK A H, et al. Adapting subject specific motor imagery EEG patterns in space-time-frequency for a brain computer interface[J]. Biomed Signal Process Control, 2009, 4(3): 236-246.
15. 唐艳.基于时间,频率和空间域的自发脑电信号提取[D].长沙:中南大学,2008.
16. SCHLÖGL A, NEUPER C, M?LLER G, et al. DatasetⅢof BCI Competition 2003[DB/OL](2008-06-24)[2012-12-25]. http://www.bbci.de/competition/.
17. HYVÄRINEN A. Fast and robust fixed-point algorithms for independent component analysis[J]. IEEE Trans Neural Netw, 1999, 10(3): 626-634.

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