Study on Steady State Visual Evoked Potential Target Detection Based on Two-dimensional Ensemble Empirical Mode Decomposition_Journal of Biomedical Engineering

Authors：

 YANGChen , HUANGLiya , WENNian , YANGJunyu

School of Electronic Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China;

Corresponding author：

YANGChen, Email: ychen21@163.com

Keywords：

brain computer interface; steady state visual evoked potential; two-dimensional ensemble empirical mode decomposition; intrinsic mode function; short-time Fourier transform; accuracy rate

DOI：

10.7507/1001-5515.20150093

Video：

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Abstract Full text Figures/Tables Video References Cited by

Brain computer interface is a control system between brain and outside devices by transforming electroencephalogram (EEG) signal. The brain computer interface system does not depend on the normal output pathways, such as peripheral nerve and muscle tissue, so it can provide a new way of the communication control for paralysis or nerve muscle damaged disabled persons. Steady state visual evoked potential (SSVEP) is one of non-invasive EEG signals, and it has been widely used in research in recent years. SSVEP is a kind of rhythmic brain activity simulated by continuous visual stimuli. SSVEP frequency is composed of a fixed visual stimulation frequency and its harmonic frequencies. The two-dimensional ensemble empirical mode decomposition (2D-EEMD) is an improved algorithm of the classical empirical mode decomposition (EMD) algorithm which extended the decomposition to two-dimensional direction. 2D-EEMD has been widely used in ocean hurricane, nuclear magnetic resonance imaging (MRI), Lena image and other related image processing fields. The present study shown in this paper initiatively applies 2D-EEMD to SSVEP. The decomposition, the 2-D picture of intrinsic mode function (IMF), can show the SSVEP frequency clearly. The SSVEP IMFs which had filtered noise and artifacts were mapped into the head picture to reflect the time changing trend of brain responding visual stimuli, and to reflect responding intension based on different brain regions. The results showed that the occipital region had the strongest response. Finally, this study used short-time Fourier transform (STFT) to detect SSVEP frequency of the 2D-EEMD reconstructed signal, and the accuracy rate increased by 16%.

Citation： YANGChen, HUANGLiya, WENNian, YANGJunyu. Study on Steady State Visual Evoked Potential Target Detection Based on Two-dimensional Ensemble Empirical Mode Decomposition. Journal of Biomedical Engineering, 2015, 32(3): 508-513. doi: 10.7507/1001-5515.20150093 Copy

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5.	HUANG N E, SHEN Z, LONG S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proc R Soc Lond A, 1998, 454(1971):903-995.
6.	HUANG Manling, WU Pingdong, LIU Ying, et al. Application and contrast in brain-computer interface between Hilbert-Huang transform and wavelet transform[C]//The 9th International Conference for Young Computer Scientists, 2008. ICYCS 2008. Hunan:2008:1706-1710.
7.	WU Zhaohua, HUANG N E. Ensemble empirical mode decomposition:a noise assisted data analysis method[J]. Adv Adapt Data Anal, 2009, 1(1):1-41.
8.	FLANDRIN P, RILLING G, GONCALVES P. Empirical mode decomposition as a filter bank[J]. IEEE Signal Process Lett, 2004, 11(2):112-114.
9.	SWEENEY K T, MCLOONE S F, WARD T E. The use of ensemble empirical mode decomposition with canonical correlation analysis as a novel artifact removal technique[J]. IEEE Trans Biomed Eng, 2013, 60(1):97-105.
10.	ZHU Xiaojun, LV Shiqin, FAN Liujuan, et al. The EEG signal process based on EEMD[C]//20112nd International Symposium on Intelligence Information Processing and Trusted Computing (IPTC). Hubei:2011:222-225.
11.	WU Zhaohua, HUANG N E, CHEN Xianyao. The multi-dimensional ensemble empirical mode decomposition method[J]. Adv Adapt Data Anal, 2009, 1(3):339-372.
12.	WANG Y, WANG Y T, JUNG T P. Visual stimulus design for high-rate SSVEP BCI[J]. Electron Lett, 2010, 46(15):1057-1058.
13.	HUANG Liya, HUANG Xiaoxia, WANG Yute, et al. Empirical mode decomposition improves detection of SSVEP[C]//201335th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Osaka:2013:3901-3904.
14.	HUANG N E, WU Zhaohua. A review on Hilbert-Huang transform:method and its applications to geophysical studies[J]. Rev Geophys, 2008, 46(2):1-23.
15.	樊刘娟.WPD-EEMD方法在脑电信号处理中的应用研究[D].太原:太原理工大学, 2013.
16.	曲丽荣.短时傅立叶变换在数字信号处理中的应用[J].科技资讯, 2007, (27):54-55.
17.	杨丽娟, 张白桦, 叶旭桢.快速傅里叶变换FFT及其应用[J].光电工程, 2004, 31(z1):1-3, 7.

1. BIN Guangyu, GAO Xiaorong, WANG Yijun, et al. VEP-based brain-computer interfaces:time, frequency, and code modulations[J]. IEEE Comput Intel Mag, 2009, 4(4):22-26.
2. WOLPAW J R, BIRBAUMER N, MCFARLAND D J, et al. Brain-computer interfaces for communication and control[J]. Clin Neurophys, 2002, 113(6):767-791.
3. 王卓.基于SSVEP的脑-机接口及在通信与拼写中的应用研究[D].南昌:南昌大学, 2013.
4. MÜLLER-PUTZ G R, SCHERER R, BRAUNEIS C, et al. Steady-state visual evoked potential (SSVEP)-based communication:impact of harmonic frequency components[J]. J Neur Eng, 2005, 2(4):123-130.
5. HUANG N E, SHEN Z, LONG S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proc R Soc Lond A, 1998, 454(1971):903-995.
6. HUANG Manling, WU Pingdong, LIU Ying, et al. Application and contrast in brain-computer interface between Hilbert-Huang transform and wavelet transform[C]//The 9th International Conference for Young Computer Scientists, 2008. ICYCS 2008. Hunan:2008:1706-1710.
7. WU Zhaohua, HUANG N E. Ensemble empirical mode decomposition:a noise assisted data analysis method[J]. Adv Adapt Data Anal, 2009, 1(1):1-41.
8. FLANDRIN P, RILLING G, GONCALVES P. Empirical mode decomposition as a filter bank[J]. IEEE Signal Process Lett, 2004, 11(2):112-114.
9. SWEENEY K T, MCLOONE S F, WARD T E. The use of ensemble empirical mode decomposition with canonical correlation analysis as a novel artifact removal technique[J]. IEEE Trans Biomed Eng, 2013, 60(1):97-105.
10. ZHU Xiaojun, LV Shiqin, FAN Liujuan, et al. The EEG signal process based on EEMD[C]//20112nd International Symposium on Intelligence Information Processing and Trusted Computing (IPTC). Hubei:2011:222-225.
11. WU Zhaohua, HUANG N E, CHEN Xianyao. The multi-dimensional ensemble empirical mode decomposition method[J]. Adv Adapt Data Anal, 2009, 1(3):339-372.
12. WANG Y, WANG Y T, JUNG T P. Visual stimulus design for high-rate SSVEP BCI[J]. Electron Lett, 2010, 46(15):1057-1058.
13. HUANG Liya, HUANG Xiaoxia, WANG Yute, et al. Empirical mode decomposition improves detection of SSVEP[C]//201335th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Osaka:2013:3901-3904.
14. HUANG N E, WU Zhaohua. A review on Hilbert-Huang transform:method and its applications to geophysical studies[J]. Rev Geophys, 2008, 46(2):1-23.
15. 樊刘娟.WPD-EEMD方法在脑电信号处理中的应用研究[D].太原:太原理工大学, 2013.
16. 曲丽荣.短时傅立叶变换在数字信号处理中的应用[J].科技资讯, 2007, (27):54-55.
17. 杨丽娟, 张白桦, 叶旭桢.快速傅里叶变换FFT及其应用[J].光电工程, 2004, 31(z1):1-3, 7.

Journal of Biomedical Engineering

Study on Steady State Visual Evoked Potential Target Detection Based on Two-dimensional Ensemble Empirical Mode Decomposition

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