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Fast Wavelet Transformation of EEG

S.J. Schiff, A. Aldroubi, M. Unser, S. Sato

Electroencephalography and Clinical Neurophysiology, vol. 91, no. 6, pp. 442-455, December 1994.

Wavelet transforms offer certain advantages over Fourier transform techniques for the analysis of EEG. Recent work has demonstrated the applicability of wavelets for both spike and seizure detection, but the computational demands have been excessive. The authors compare the quality of feature extraction of continuous wavelet transforms using standard numerical techniques, with more rapid algorithms utilizing both polynomial splines and multiresolution frameworks. They further contrast the difference between filtering with and without the use of surrogate data to model background noise, demonstrate the preservation of feature extraction with critical versus redundant sampling, and perform the analyses with wavelets of different shape. Comparison is made with windowed Fourier transforms, similarly filtered, at different data window lengths. The authors here report a dramatic reduction in computational time required to perform this analysis, without compromising the accuracy of feature extraction. It now appears technically feasible to filter and decompose EEG using wavelet transforms in real time with ordinary microprocessors.

AUTHOR="Schiff, S.J. and Aldroubi, A. and Unser, M. and Sato, S.",
TITLE="Fast Wavelet Transformation of {EEG}",
JOURNAL="Electroencephalography and Clinical Neurophysiology",

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