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Currently, event-related potential (ERP) signals are analysed in the time domain (ERP technique) or in the frequency domain (Fourier analysis and variants). In techniques of time-domain and frequency-domain analysis (short-time Fourier transform, wavelet transform) assumptions concerning linearity, stationarity, and templates are made about the brain signals. In the time-frequency component analyser (TFCA), the assumption is that the signal has one or more components with non-overlapping supports in the time-frequency plane. In this study, the TFCA technique was applied to ERPs. TFCA determined and extracted the oscillatory components from the signal and, simultaneously, localized them in the time-frequency plane with high resolution and negligible cross-term contamination. The results obtained by means of TFCA were compared with those obtained by means of other commonly used techniques of ERP analysis, such as bilinear time-frequency distributions and wavelet analysis. It is suggested that TFCA may serve as an appropriate tool for capturing the localized ERP components in the time-frequency domain and for studying the intricate, frequency-based dynamics of the human brain.

In this paper, we generalize the optimal deghosting (ODG) method used for deghosting over/under data to combine pressure (P) and vertical velocity (Z) data recorded with a multi-component streamer to minimize the impact of the noise on the deghosted data. The ODG approach uses pressure and velocity ghost models and the statistics of the residual noise to minimize, in a least-squares sense, the noise on the up-going/ deghosted wavefield. ODG and the standard PZ summation (PZSUM) combinations are applied to pressure and velocity data recorded in the North Sea. We show that both methods attenuate the receiver ghost, fill in information at the pressure notch frequencies and that ODG has the least post-combination noise level. We also show pre-and post-stack vertical velocity data with encouraging signal-to-noise ratios. Finally, in order to further improve the PZ deghosted data, we suggest a toolbox approach that takes advantage of both ODG and PZSUM combinations and accounts for the varying signal-to-noise ratios observed on multi-component streamer data.

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