Abstract
Visual evoked potentials (VEPs) are scalp electrical signals generated in response to rapid and repetitive visual stimuli. These signals possess complex time-frequency structures and are difficult to characterize with conventional methods. In this chapter, we propose a new approach based on the adaptive chirplet transform (ACT) that can represent a complete VEP response from the transient to the steady-state portion. Our implementation involves both a non-windowed and windowed approach. The non-windowed ACT employs a coarse-refinement algorithm (MPLEM) to estimate multiple chirplets under low signal-to-noise ratio condition. We show how the chirplet parameters (i.e., time-spread, chirp rate, time-center and frequency-center) can be used to separate the transient from the steady-state portions of the response, and that as few as three chirplets are required to represent a complete VEP signal. The windowed approach is implemented by partitioning the signal into equal-length non-overlap** segments before estimating a single chirplet from each segment, resulting in significant reduction of computational time. The application of the windowed ACT to VEP analysis is also discussed.
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Notes
- 1.
It should be emphasized that the term “transient VEP,” or tVEP, employed here is conceptually different from that used in traditional electrophysiological literature. It usually refers to an experimental paradigm where the potentials are evoked by visual stimuli which are sufficiently widely spaced so that the visual system can be regarded as returning to a state of rest between successive stimuli [34]. In this chapter, however, tVEP refers to the signal prior to the formation of steady-state VEP.
References
Akay, M. (1998). Time-frequency and wavelets in biomedical signal processing. New York: IEEE Press.
Aunon, J. I., McGillem, C. D., & Childers, D. G. (1981). Signal processing in evoked-potential research – averaging and modeling. Crc Critical Reviews in Bioengineering, 5(4), 323–367.
Boushash, B., & Whitehouse, H. J. (1986). Seismic applications of the Wigner-Ville distribution. Paper presented at the Proceedings of IEEE International Conference on Circuits System.
Bultan, A. (1999). A four-parameter atomic decomposition of chirplets. IEEE Transactions on Signal Processing, 47(3), 731–745.
Cheng, M., Gao, X. R., Gao, S. G., & Xu, D. F. (2002). Design and implementation of a brain-computer interface with high transfer rates. IEEE Transactions on Biomedical Engineering, 49(10), 1181–1186.
Coelho, F., Simpson, D., & Infantosi, A. (1995). Testing recruitment in the EEG under repetitive photo stimulation using frequency-domain approaches. Paper presented at the IEEE-EMBC and CMBEC, Montreal, Canada, 905–906.
Cohen, L. (1995). Time-frequency analysis. Englewood Cliffs, N.J: Prentice Hall PTR.
Cui, J. (2006). Adaptive chirplet transform for the analysis of visual evoked potentials. Unpublished PhD Dissertation, University of Toronto, Toronto.
Cui, J., & Wong, W. (2006). The adaptive chirplet transform and visual evoked potentials. IEEE Transactions on Biomedical Engineering, 53(7), 1378–1384.
Cui, J., Wong, W., & Mann, S. (2005). Time-frequency analysis of visual evoked potentials using chirplet transform. Electronics Letters, 41(4), 217–218.
Daubechies, I. (1992). Ten lectures on wavelets. Philadelphia, PA: Society for Industrial and Applied Mathematics.
Desmedt, J. E. (1990). Visual evoked potentials. Amsterdam, New York, Oxford: Elsevier Science Publishers B.V. (Biomedical Division).
Di Russo, F., & Spinelli, D. (2002). Effects of sustained, voluntary attention on amplitude and latency of steady-state visual evoked potential: A costs and benefits analysis. Clinical Neurophysiology, 113(11), 1771–1777.
Drouiche, K. (2000). A new test for whiteness. IEEE Transactions on Signal Processing, 48(7), 1864–1871.
Fricker, S. J. (1962). Narrow-band filter techniques for detection and measurement of evoked responses. Electroencephalography and Clinical Neurophysiology, 14(3), 411–421.
Gabor, D. (1946). Theory of communication. Journal of IEE, 93(26), 429–457.
Halliday, A. M. (1993). Evoked potentials in clinical testing. Edinburgh: Churchill Livingstone.
Harding, G. F. A. (1974). The visual evoked response. In M. J. Roper-Hall (Ed.), Advances in Ophthalmology (pp. 2–28). Basel: S Kaerger AG.
Heckenlively, J. R., & Arden, G. B. (2006). Principles and practice of clinical electrophysiology of vision (2nd ed.). Cambridge, Mass.: MIT Press.
Kay, S. M. (1993). Fundamentals of statistical signal processing: Estimation and detection theory. Englewood Cliffs, N.J.: Prentice-Hall PTR.
Liavas, A. P., Moustakides, G. V., Henning, G., Psarakis, E. Z., & Husar, P. (1998). A periodogram-based method for the detection of steady-state visually evoked potentials. IEEE Transactions on Biomedical Engineering, 45(2), 242–248.
Mallat, S. G., & Zhang, Z. (1993). Matching pursuit with time-frequency dictionaries. IEEE Transactions on Signal Processing, 41(12), 3397–3415.
Mann, S., & Haykin, S. (1991, June 1991). The chirplet transform: A generalization of Gabor’s logon transform. Paper presented at the Vision Interface, Calgary, Canada.
Mann, S., & Haykin, S. (1992). Adaptive chirplet transform - An adaptive generalization of the wavelet transform. Optical Engineering, 31(6), 1243–1256.
Mann, S., & Haykin, S. (1995). The chirplet transform - physical considerations. IEEE Transactions on Signal Processing, 43(11), 2745–2761.
Mast, J., & Victor, J. D. (1991). Fluctuations of steady-state VEPs – Interaction of driven evoked-potentials and the EEG. Electroencephalography and Clinical Neurophysiology, 78(5), 389–401.
Middendorf, M., McMillan, G., Calhoun, G., & Jones, K. S. (2000). Brain-computer interfaces based on the steady-state visual-evoked response. IEEE Transactions on Rehabilitation Engineering, 8(2), 211–214.
Norcia, A. M., & Tyler, C. W. (1985). Spatial-frequency sweep VEP – visual-acuity during the 1st year of life. Vision Research, 25(10), 1399–1408.
Pei, F., Pettet, M. W., & Norcia, A. M. (2002). Neural correlates of object-based attention. Journal of Vision, 2(9), 588–596.
Qian, S., & Chen, D. P. (1994). Signal representation using adaptive normalized Gaussian functions. Signal Processing, 36(1), 1–11.
Qian, S., Dunham, M. E., & Freeman, M. J. (1995). Transionospheric signal recognition by joint time-frequency representation. Radio Science, 30(6), 1817–1829.
Regan, D. (1966). Some characteristics of average steady-state and transient responses evoked by modulated light. Electroencephalography and Clinical Neurophysiology, 20(3), 238–248.
Regan, D. (1972). Evoked potentials in psychology, sensory physiology and clinical medicine. London: Chapman and Hall.
Regan, D. (1989). Human brain electrophysiology: Evoked potentials and evoked magnetic fields in science and medicine. New York: Elsevier.
Tang, Y., & Norcia, A. M. (1995). Application of adaptive filtering to steady-state evoked response. Medical & Biological Engineering & Computing, 33(3), 391–395.
Van Der Tweel, L. H. (1964). Relation between psychophysics and electrophysiology of flicker. Documenta Ophthalmologica,, 18 287–304.
Victor, J. D., & Mast, J. (1991). A New Statistic for Steady-State Evoked-Potentials. Electroencephalography and Clinical Neurophysiology, 78(5), 378–388.
Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., & Vaughan, T. M. (2002). Brain-computer interfaces for communication and control. Clinical Neurophysiology, 113(6), 767–791.
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Cui, J., Wong, W. (2009). Visual Evoked Potential Analysis Using Adaptive Chirplet Transform. In: Naït-Ali, A. (eds) Advanced Biosignal Processing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89506-0_11
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