P3 Component Detection Using HHT

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• 作者：Tomá? Prokop (23)
  Roman Mou?ek (23)
  
• 关键词：Electroencephalography ; EEG signal processing ; ERP detection ; P3 component ; Hilbert ; Huang transform ; HHT ; Empirical Mode Decomposition ; EMD ; stopping criteria
• 刊名：Lecture Notes in Computer Science
• 出版年：2014
• 出版时间：2014
• 年：2014
• 卷：8609
• 期：1
• 页码：100-110
• 全文大小：402 KB
• 参考文献：1. Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.C., Tung, C.C., Liu, H.H.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London Series A: Mathematical, Physical and Engineering Sciences?454, 903-95 (1971,1998)
  2. Liu, R.: Empirical mode decomposition: A useful technique for neuroscience? (2002)
  3. Yang, Z., Yang, L.: A new definition of the intrinsic mode function. A new definition of the intrinsic mode function. World Academy of Science, Engineering and Technology?60, 822-25 (2009)
  4. Ciniburk, J.: Hilbert-Huang Transform for ERP Detection. Ph.D. thesis, Faculty of Applied Sciences, University of West Bohemia, Univerzitni 22, 306 14 Pilsen (2011)
  5. Huang, N., Attoh-Okin, N.O.: The Hilbert-Huang Transform in Engineering. CRC Press (2005)
  6. Huang, N.E., et al.: The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society A: Mathematical?454 (1998)
  7. Liang, H., Bresser, S., Desimone, R.: Empirical mode decomposition: a method for analyzing neural data. Neurocomputing?65, 801-07 (2005) CrossRef
  8. ?ondík, T., Ciniburk, J., Mautner, P., Mou?ek, R.: Erp components detection using wavelet transform and matching pursuit algorithm. In: Proceedings of 3rd Driver Car Interaction Interface 2010 Conference (2010)
  9. EEG/ERP Portal (2013), http://eegdatabase.kiv.zcu.cz/
  
• 作者单位：Tomá? Prokop (23)
  Roman Mou?ek (23)
  
  23. Department of Computer Science and Engineering, University of West Bohemia, Univerzitní 8, 306 14, Pilsen, Czech Republic
  
• ISSN：1611-3349

文摘

This paper describes improvement of the Hilbert-Huang transform (HHT) for detection of ERP components in the EEG signal. Time-frequency domain methods, such as the wavelet transform or matching pursuit, are commonly for this task. We used a modified Hilbert-Huang transform that allows the processing of quasi-stationary signals such as EEG. The essential part of the HHT is an Empirical Mode Decomposition (EMD) that decomposes signal into intrinsic mode functions (IMFs). We designed additional stopping criteria for better selection of IMFs in the EMD. These IMFs positively affect later computed instantaneous attributes and increase classification success. We tested the influence of additional stopping criteria on classification reliability using the real EEG data acquired in our laboratory. Our results demonstrated that we were able to detect the P3 component by using the HHT with additional stopping criteria more successfully than by using the original implementation of modified HHT, continuous wavelet transform and matching pursuit.