Skip to main content
SpringerLink
Log in

Single-Trial Estimation of Evoked Potential Signals via ARX Model and Sparse Coding

  • Original Article
  • Published:
Journal of Medical and Biological Engineering Aims and scope Submit manuscript

Abstract

This paper presents a single-trial evoked potential (EP) estimation method based on an autoregressive model with exogenous input modeling and sparse coding. This method uses sparse coding instead of the autoregressive-moving-average model to model EPs, as the former is more flexible. The best matching atoms from the dictionary are used to represent the EP signal without needing to estimate the number of atoms beforehand. By transforming the electroencephalography signal into white noise, the single-trial EP estimation is transformed into a signal denoising problem for white noise. With the dictionary constructed specially for EPs, the EP signal can be extracted easily with sparse coding. Moreover, since the location of the atom in the dictionary has no influence on the effectiveness of sparse decomposition, variations of the amplitude and latency of EPs have only a minor impact on the performance of the proposed method. The proposed method can thus track EP signal variations. Experimental results also demonstrate that this method is effective.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Costa, M. H. (2012). Estimation of the noise autocorrelation function in auditory evoked potential applications. Biomedical Signal Processing and Control, 7, 542–548.

    Article  Google Scholar 

  2. Reynolds, C., Osuagwu, B. A., & Vuckovic, A. (2015). Influence of motor imagination on cortical activation during functional electrical stimulation. Clinical Neurophysiology, 126, 1360–1369.

    Article  Google Scholar 

  3. Paul, J. S., Luft, A. R., Hanley, D. F., & Thakor, N. V. (2001). Coherence-weighted Wiener filtering of somatosensory evoked potentials. IEEE Transactions on Biomedical Engineering, 48(12), 1483–1488.

    Article  Google Scholar 

  4. Georgiadis, S. D., Ranta-aho, P. O., Tarvainen, M. P., & Karjalainen, P. A. (2005). Single-trial dynamical estimation of event-related potentials: a Talman filter-based approach. IEEE Transactions on Biomedical Engineering, 52(8), 1397–1406.

    Article  Google Scholar 

  5. Wang, Y. X., Qiu, T. S., & Liu, R. (2012). Few-sweep estimation of evoked potential based on a generalized subspace approach. Biomedical Signal Processing and Control, 7(8), 185–191.

    Article  Google Scholar 

  6. Lange, D. H., Pratt, H., & Indar, G. F. (1997). Modeling and estimation of single evoked brain potential components. IEEE Transactions on Biomedical Engineering, 44(9), 791–799.

    Article  Google Scholar 

  7. Garoosi, V., & Jansen, B. H. (2000). Development and evaluation of the piecewise Prony method for evoked potential analysis. IEEE Transactions on Biomedical Engineering, 47(12), 1549–1554.

    Article  Google Scholar 

  8. Wang, Z., & Roe, A. W. (2011). Trial-to-trial noise cancellation of cortical field potentials in awake macaques by autoregression model with exogenous input (ARX). Journal of Neuroscience Methods, 194, 266–273.

    Article  Google Scholar 

  9. Cerutti, S., Baselli, G., Liberati, D., & Pavesi, G. (1987). Single sweep analysis of visual evoked potentials through a model of parametric identification. Biological Cybernetics, 56, 111–120.

    Article  Google Scholar 

  10. Kumru, H., Soler, D., Vidal, J., Tormos, J. M., Pascual-Leone, A., & Valls-Sole, J. (2012). Evoked potentials and quantitative thermal testing in spinal cord injury patients with chronic neuropathic pain. Clinical Neurophysiology, 123, 598–604.

    Article  Google Scholar 

  11. Silva, A. C. D., Sinclair, N. C., & Liley, D. (2012). Limitations in the Rapid extraction of evoked potentials using parametric modeling. IEEE Transactions on Biomedical Engineering, 59(5), 1462–1471.

    Article  Google Scholar 

  12. Yu, N. N., Liu, H. K., Wang, X. Y., & Lu, H. B. (2013). A joint sparse representation-based method for double-trial evoked potentials estimation. Computers in Biology and Medicine, 43(12), 2071–2078.

    Article  Google Scholar 

  13. Yu, N. N., Ding, Q. S., & Lu, H. B. (2015). Single-trial evoked potentials extraction based on sparsifying transforms. Journal of Pharmacy and Pharmacology, 3, 556–561.

    Google Scholar 

  14. Corbier, C., & Carmona, J. (2012). Robust final prediction error criterion for control oriented models validation using L2–L1 norm, In 2nd International Conference on Communications, Computing and Control Applications (CCCA), Marseilles, France, (pp. 1–6).

  15. Song, I., Park, P. G., & Newcomb, R. W. (2013). A normalized least mean squares algorithm with a step-size scaler against impulsive measurement noise. IEEE Transactions on Circuits and Systems II: Express Briefs, 60(7), 442–445.

    Article  Google Scholar 

  16. Han, M., & Sun, L. L. (2010). EEG signal classification for epilepsy diagnosis based on AR model and RVM. In International Conference on Intelligent Control and Information Processing (ICICIP), Dalian, China, (pp. 134–139).

  17. Zhang, Y., Ji, X., & Zhang, Y. (2015). Classification of EEG signals based on AR model and approximate entropy. In International Joint Conference on Neural Networks (IJCNN), Killarney, Ireland, (pp. 1–6).

  18. Shental, O. (2011). Sparse Representation of White Gaussian Noise with Application to `0-Norm Decoding in Noisy Compressed Sensing, (pp. 1–7). eprint arXiv:1104.2215.

  19. Chen, S. S., Donoho, D. L., & Saunders, M. A. (2001). Atomic decomposition by basis pursuit. SIAM Review, 43, 129–159.

    Article  MathSciNet  MATH  Google Scholar 

  20. Pati, Y. C. Rezaiifar, R., & Trishnaprasad, P. S. (1993) Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition. In Conference on Record of 27th Asilomar Conference Signals System Computing (Vol. 1, pp. 40–44).

  21. Yuan, L., Liu, J., & Ye, J. (2013). Efficient methods for overlapping group lasso. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(9), 2104–2116.

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the Nature Science Foundation of China (Grant No. 61401181) and the Science and Technology Innovation Project of Xuzhou, China (Grant No. XZKJ8386).

Author information

Authors and Affiliations

  1. School of Electrical Engineering and Automation, Jiangsu Normal University, Xuzhou, 221116, China

    Nannan Yu & Qisheng Ding

  2. Department of Internal Neurology, Xuzhou Central Hospital, Xuzhou, 221116, China

    Hanbing Lu

Authors
  1. Nannan Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qisheng Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hanbing Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hanbing Lu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yu, N., Ding, Q. & Lu, H. Single-Trial Estimation of Evoked Potential Signals via ARX Model and Sparse Coding. J. Med. Biol. Eng. 37, 209–219 (2017). https://doi.org/10.1007/s40846-016-0209-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40846-016-0209-x

Keywords

Search

Navigation