Abstract
A challenging task in psychophysiology is the extraction of event-related potentials (ERPs) from the background electro-encephalogram. The task is made more difficult by the properties of ERPs, which typically consist of multiple features of variable latency, localised in time and frequency. A novel technique is described for analysis of ERPs, adaptive wavelet filtering (AWF), which is proposed as an alternative to trial averaging. Band-limited detail representations of each trial are obtained using wavelet analysis. The Woody adaptive filter is then used to align trials with respect to the evoked response. In a simulation study, the AWF extracts 39% of higher-frequency signal variance from background noise, compared with less than 1% for standard averaging and the Woody filter. The AWF is applied to a data-set of 448 ERPs, comprising right-finger button presses from eight subjects. Average split-half reliability of the AWF on scales up to 12 Hz was 0.51.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Coifman, R. R., andWickerhauser, M. V. (1992): ‘Entropy-based algorithms for best basis selection’,J. IEEE Trans. Inf. Theory,38, pp. 712–718
Coles, M. G. H. (1989): ‘Modern mind-brain reading: Psychophysiology, physiology, and cognition’,Psychophys.,26, pp. 251–269
Cutmore, R. H., andJames, D. A. (1999): ‘Identifying and reducing noise in psychophysiological recordings’,Int. J. Psychophys.,32, pp. 129–150
Daubechies, I. (1992), ‘Ten lectures on wavelets’ (CBMS-NSF Series on Applied Mathematics, SIAM)
Deecke, L., Scheid, P., andKornhuber, H. H. (1969): ‘Distribution of readiness potential, pre-motion positivity, and motor potential of the human cerebral cortex preceding voluntary finger movements’,Exp. Brain Res.,7, pp. 158–168
Horst, R. L., andDonchin, E. (1980): ‘Beyond averaging. II. Single-trial classification of exogenous event-related potentials using step-wise discriminant analysis’,Electroenceph. Clin. Neurophys.,48, pp. 113–126
Jasper, H. H. (1958): ‘The ten twenty electrode system of the international federation’,Electroenceph. Clin. Neurophys.,43, pp. 397–403
John, E. R., andPrichep, L. S. (1993): ‘Principles of neurometric analysis of EEG and evoked potentials’ inNiedermeyer, E., andLopes Da Silva, F. H. (Eds): ‘Electroencephalography: basic principles, clinical applications, and related fields’ (Williams & Williams, Baltimore), pp. 989–1002
Kaiser, G. (1994): ‘A friendly guide to wavelets’ (Birkhauser, Boston)
Lopes Da Silva, F. H. (1993): ‘Dynamics of EEGs as signals of neuronal populations: Models and theoretical considerations’ inNiedermeyer, E., andLopes Da Silva, F. H. (Eds): ‘Electroencephalography: basic principles, clinical applications, and related fields” (Williams & Williams, Baltimore) pp. 63–77
Mallat, S. (1998): ‘A wavelet tour of signal processing’ (Academic, San Diego)
Nievergelt, Y. (1999): ‘Wavelets made easy’ (Birkhauser, Boston)
Pfurtscheller, G., Steffan, J. andMaresch, H. (1988): ‘ERD-mapping and functional topography-temporal and spatial aspects’ inPfurtscheller, G., andLopes Da Silva, F. H. (Eds): ‘Functional brain imaging’ (Huber, Toronto), pp. 117–130
Pfurtscheller, G., andBerghold, A. (1989): ‘Patterns of cortical activation during planning of voluntary movement’,Electroenceph. Clin. Neurophys.,72, pp. 250–258
Pfurtscheller, G. andKlimesch, W. (1991): ‘Event-related desynchronization during motor behavior and visual information processing’ inBrunia, C. H. M., Mulder, G., andVerb, M. N. (Eds): ‘Event-related brain research EEG, Suppl. 42’ (Elsevier, Amsterdam), pp. 58–65
Pfurtscheller, G. (1992): ‘Event-related synchronization (ERS): an electrophysiological correlation of cortical areas at rest’,Electroenceph. Clin. Neurophys.,83, pp. 62–69
Pfurtscheller, G., Flotzinger, D., andNeuper, C. (1994): ‘Differentiation between finger, toe and tongue movement in man based on 40 Hz EEG’,Electroenceph. Clin. Neurophys.,90, pp. 456–460
Raz, J., Dickerson, L., andTuretsky, B. (1999): ‘A wavelet packet model of evoked potentials’,Brain Lang.,66, pp. 61–88
Samar, V. J., Bopardikar, A., Rao, R., andSwartz, K. (1999): ‘Wavelet analysis of neuroelectric waveforms: A conceptual tutorial’,Brain Lang.,66, pp. 7–60
Sano, N., andCoifman, R. R. (1994): ‘Local discriminant bases’ inLaine, A. F., andUnser, M. A. (Eds): ‘Mathematical imaging: Wavelet applications in signal and image processing II’,Proc. SPIE,230, pp. 2–14
Storiade, M. (1993): ‘Cellular substrates of brain rhythms’ inNiedermeyer, E., andLopes Da Silva, F. H. (Eds): ‘Electroencephalography: basic principles, clinical applications, and related fields’ (Williams & Williams, Baltimore), pp. 27–62
Trejo, L. J., andShensa, M. J. (1993): ‘Linear and neural network models for predicting human signal detection performance from event-related potentials: A comparison of the wavelet transform with other feature extraction methods’. Proc. 5th Workshop Neural Networks SPIE,2204, pp. 153–161
Wastell, D. G. (1977): ‘Statistical detection of individual evoked responses: An evaluation of Woody's adaptive filter’,Electroenceph. Clin. Neurophys.,42, pp. 835–839
Woody, C. D. (1967): ‘Characterization of an adaptive filter for the analysis of variable latency neuroelectric signals’,Med. Biol. Eng.,5, pp 539–553
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Browne, M., Cutmore, T.R.H. Adaptive wavelet filtering for analysis of event-related potentials from the electro-encephalogram. Med. Biol. Eng. Comput. 38, 645–652 (2000). https://doi.org/10.1007/BF02344870
Received:
Accepted:
Issue Date:
DOI: https://doi.org/10.1007/BF02344870