Abstract
Steady-state evoked potentials (SSEP) are the electrical activity recorded from the scalp in response to high-rate sensory stimulation. SSEP consist of a constituent frequency component matching the stimulation rate, whose amplitude and phase remain constant with time and are sensitive to functional changes in the stimulated sensory system. Monitoring SSEP during neurosurgical procedures allows identification of an emerging impairment early enough before the damage becomes permanent. In routine practice, SSEP are extracted by averaging of the EEG recordings, allowing detection of neurological changes within approximately a minute. As an alternative to the relatively slow-responding empirical averaging, we present an algorithm that detects changes in the SSEP within seconds. Our system alerts when changes in the SSEP are detected by applying a two-step Generalized Likelihood Ratio Test (GLRT) on the unaveraged EEG recordings. This approach outperforms conventional detection and provides the monitor with a statistical measure of the likelihood that a change occurred, thus enhancing its sensitivity and reliability. The system’s performance is analyzed using Monte Carlo simulations and tested on real EEG data recorded under coma.
Similar content being viewed by others
References
Bach MMT, Meigen T (1999) Do’s and don’ts in fourier analysis of steady-state potentials. Doc Ophthalmol 99: 69–82
Basseville M, Nikiforov IV (1993) Detection of abrupt changes: theory and applications. Prentice-Hall, Englewood Cliffs
Bender R, Schultz B, Schultz A, Pichlmayr I (1992) Testing the gaussianity of the human eeg during anasthesia. Methods Inf Med 31(1): 56–59
Bergholz R, Lehmann TN, Fritz G, Ruther K (2008) Fourier transformed steady-state flash evoked potentials for continuous monitoring of visual pathway function. Doc Ophthalmol 116: 217–229
Burke D, Nuwer M, Daube J, Fischer C, Schramm J, Yingling CD, Jones S (1999) Intraoperative monitoring. Electroencephalogr Clin Neurophysiol 52: 133–148
Chiappa K (1997) Evoked potentials in clinical medicine. Lippincott-Raven, Philadelphia
Davila C, Srebro R (2000) Subspace averaging of steady-state visual evoked potentials. IEEE Trans Bio-Med Eng 47: 720–728
Davilla C, Ghaleb I, Serebro R (1997) Prewhitening of background brain activity via autoregressive modeling. Biomedical engineering conference, 1997, Proceedings of the 1997 sixteenth southern pp 242–245
Fisher RA (1925) Theory of statistical estimation. Proc Camb Philos Soc 22: 700–725
Friman O, Luth T, Volosyak I, Graser A (2007) Spelling with steady-state visual evoked potentials. 3rd International IEEE/EMBS conference on neural engineering, pp 354–357
Gersch W (1970) Spectral analysis of EEGs by autoregressive decomposition of time series. Math Biosci 7: 205–222
Gevins AS (1984) Analysis of electromagnetic signals of the human brain: Milestones, obstacles, and goals. IEEE Trans Biomed Eng 31: 833–850
Heckenlively JR, Arden GB (1991) Principles and practice of clinical electrophysiology of vision. Mosby, St Louis
Kay SM (1988) Modern spectral estimation. Prentice-Hall, Englewood Cliffs
Lehmann EL (1991) Testing statistical hypothesis. Statistical/probability series. Wadsworth and Brooks/Cole, Pacific Grove
Lorden G (1971) Procedures for reacting to a change in distribution. Ann Math Stat 42: 1897–1908
McGillem CD, Aunon JI, Yu KB (1985) Signal and noise in evoked potentials. IEEE Trans Biomed Eng 32: 1012–1016
Middendorf M, McMillan G, Calhoun G, Jones KS (2000) Brain–computer interfaces based on the steady-state visual-evoked response. IEEE Trans Neural Syst Rehabil Eng 8: 217–229
Nuwer M, Daube J, Fischer C, Schramm J, Yingling C (1993) Neuromonitoring during Surgery, Report of an IFCN Committee. Electroencephalogr Clin Neurophysiol 87(5): 273–276
Nuwer MR, Dawson EG, Carlson LG, Kanim L, Sherman JE (1995) Somatosensory evoked potential monitoring reduces neurologic deficits after scoliosis surgery: Results of a large multicenter survey. Electroencephalogr Clin Neurophysiol 96: 6–11
Orfanidis SJ (1996) Optimum signal processing. An introduction. 2. Prentice-Hall, Englewood Cliffs
Pratt H, Matrin WH, Bleich N, Zaaroor M, Shacham SE (1994) High intensity, goggle-mounted flash stimulator for short-latency visual evoked potentials. Electroencephalogr Clin Neurophysiol 92: 469–472
Robey FC, Fuhrmann DR, Nitzberg R, Kelly EJ (1992) A CFAR adaptive matched filter detector. IEEE Trans Aerosp Electron Syst 28: 208–216
Schacham SE, Pratt H (1985) Detection and measurement of steady-state evoked potentials using a lock-in amplifier. J Neuroserg 62: 935–938
Scofield JH (1994) Frequency-domain description of a lock-in amplifier. Am J Phys 62((2): 129–133
Wiedemayer H, Fauser B, Sandalcioglu IE, Armbruster W, Stolke D (2004) Observations on intraoperative monitoring of visual pathways using steady-state visual evoked potentials. Eur J Anaesthesiol 21: 429–433
Zaaroor M, Pratt H, Feinsod M, Shacham SE (1993) Real time monitoring of steady-state visual evoked potentials. Isr J Med Sci 29(1): 17–22
Zetterberg LH (1969) Estimation of parameters for a linear difference equation wih applications to EEG analysis. Math Biosci 5: 227–275
Author information
Authors and Affiliations
Corresponding author
Additional information
Hillel Pratt and Menashe Zaaroor contributed equally to this study.
Rights and permissions
About this article
Cite this article
Nave, G., Eldar, Y.C., Inbar, G. et al. Real-time change detection of steady-state evoked potentials. Biol Cybern 107, 49–59 (2013). https://doi.org/10.1007/s00422-012-0523-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00422-012-0523-5