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A recently developed fragmentary decomposition method is employed to analyse single-trial event-related potentials (ERPs), thereby extending the traditional method of averaging. Using a conventional auditory oddball paradigm with 40 target stimuli, single-trial ERPs in 40 normal subjects were analysed for midline scalp (Fz, Cz and Pz) recording sites. The normalization effect, reported in our previous study of eye blink EMGs and proposed to be a characteristic property of a wide class of non-stationary physiological processes, was found to apply to these single-trial ERPs. Fragmentary decomposition of single-trial ERPs may be regarded as re-statement of the normalization effect. This allows both pre-stimulus EEGs and post-stimulus ERPs to be regarded as overlapping generic mass potentials (GMPs), with a characteristic Gaussian amplitude spectrum. On theoretical and empirical grounds we uniquely deduce a model GMP using an introduced d" function, and physically support it by the resting and transient conditions. The model takes into account the shape of the component, which suggests a simple relationship between the peak latency and the time of the component onset. Given that GMPs may be manipulated and sorted out, we present principles of the fragmentary synthesis, i.e. probabilistic ERP reconstructions on the basis of individual and ensemble properties of its identified components. Summarizing the component quantification in the form of the dynamic model provides for the first time the opportunity to quantify all significant components in single-trial ERPs. This method of single-trial analysis opens up new possibilities of exploring the dynamical ERP changes within a recording trial, particularly in late component "cognitive" paradigms.

The quantified analysis of the electroencephalogram (qEEG) has enabled the extraction of additional psychophysiological information from the raw EEG, but in turn has introduced a number of distortions. This study compared Dynamic Spectral Analysis (DSA), a novel and mathematically stringent technique for the evaluation of qEEG activity with conventional power spectral analysis in subjects with both first episode and chronic schizophrenia and matched controls. Advantages of the technique in the automated processing of data, rejection of artefact, avoidance of artefact introduced by the mathematical trans-formation of the data and the identification of irregular low frequency artefactual activity "pi" are discussed in detail. Using this method, the study has confirmed past observations of increased slow wave activity in schizophrenia, and identified a decrease in peak frequency in the alpha band in the subjects with chronic schizophrenia. The two clinical groups differed in mean peak frequency in the delta band with the first episode schizophrenia subjects having a raised mean peak frequency and the subjects with chronic schizophrenia having a lowered mean peak frequency. The results suggest continued change in the EEG with illness chronicity in schizophrenia. These changes were most evident in the frequency domain emphasizing the importance of routine measurement of mean band frequencies in qEEG studies.

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