On the positivity of the Wigner-Ville spectrum
Signal Processing
Time-frequency signal processing based on the Wigner-Weyl framework
Signal Processing
Time-frequency analysis: theory and applications
Time-frequency analysis: theory and applications
Scale and Translation Invariant Methods for Enhanced Time-FrequencyPattern Recognition
Multidimensional Systems and Signal Processing - Special issue on recent developments in time-frequency analysis
Detection, Estimation, and Modulation Theory: Radar-Sonar Signal Processing and Gaussian Signals in Noise
Signal De-Noising using the Wavelet Transform and Regularization
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 3 - Volume 3
IEEE Transactions on Signal Processing
A multiple window method for estimation of peaked spectra
IEEE Transactions on Signal Processing
Computational Intelligence and Neuroscience - EEG/MEG Signal Processing
Hi-index | 0.00 |
We present a new method for signal extraction from noisy multichannel epileptic seizure onset EEG signals. These signals are non-stationary which makes time-invariant filtering unsuitable. The new method assumes a signal model and performs denoising by filtering the signal of each channel using a time-variable filter which is an estimate of the Wiener filter. The approximate Wiener filters are obtained using the time-frequency coherence functions between all channel pairs, and a fix-point algorithm. We estimate the coherence functions using the multiple window method, after which the fix-point algorithm is applied. Simulations indicate that this method improves upon its restriction to assumed stationary signals for realistically non-stationary data, in terms of mean square error, and we show that it can also be used for time-frequency representation of noisy multichannel signals. The method was applied to two epileptic seizure onset signals, and it turned out that the most informative output of the method are the filters themselves studied in the time-frequency domain. They seem to reveal hidden features of the epileptic signal which are otherwise invisible. This algorithm can be used as preprocessing for seizure onset EEG signals prior to time-frequency representation and manual or algorithmic pattern classification.