FAST WAVELET TRANSFORMATION OF EEG

Wavelet transforms offer certain advantages over Fourier transform tec hniques for the analysis of EEG. Recent work has demonstrated the appl icability of wavelets for both spike and seizure detection, but the co mputational demands have been excessive. We compare the quality of fea ture extraction of continuous wavelet transforms using standard numeri cal techniques, with more rapid algorithms utilizing both polynomial s plines and multiresolution frameworks. We further contrast the differe nce between filtering with and without the use of surrogate data to mo del background noise, demonstrate the preservation of feature extracti on with critical versus redundant sampling, and perform the analyses w ith wavelets of different shape. Comparison is made with windowed Four ier transforms, similarly filtered, at different data window lengths. We here report a dramatic reduction in computational time required to perform this analysis, without compromising the accuracy of feature ex traction. It now appears technically feasible to filter and decompose EEG using wavelet transforms in real time with ordinary microprocessor s.