A comparative study of spectral estimation techniques for noisy non-stationary signals with application to EEG data

The paper considers the problem of spectral estimation of noisy non-stationary signals with application to electroencephalogram (EEG) data. Four well known methods for estimating the time-varying spectrum of a non-stationary signal are first reviewed and their performance compared. These methods which work well when the signal-to-noise ratio (SNR) is high, are shown to fail with varying degrees as SNR decreases. A technique for preprocessing noisy EEG data called time-frequency peak filtering (TFPF) is then presented and used to process EEG signals whose spectral content are highly non-stationary and difficult to model. It is shown that marked improvement in spectral estimates result after using the TFPF method