EEG signal decomposition and improved spectral analysis using wavelet transform

EEG, as a noninvasive testing method, plays a key role in the diagnosing diseases, and is useful for both physiological research and medical applications. Wavelet transform (WT) is a new multiresolution time-frequency analysis method. WT possesses localization feature both in time and frequency domains. It acts as a group of band-pass filters decompose mixed signal into signals at frequency bands. Using the dyadic wavelet transform, the EEG signals are successfully decomposed and denoised. In this paper we also use a ´quasi-detrending´ method for classification of EEG spectrum where the level of detrending or differencing is made to vary. Difference in time domain acts as a high pass filter in the frequency domain. Therefore the low frequency values in the delta range can be ignored and this is a saving in computation time since delta range values do not correspond to any normal conscious human mental tasks. We also show that using discrete PSD (power spectral densities) values in the range below 30 Hz gives better classification results than using the delta, theta, alpha and beta power band values used by some authors.