In a delayed matching-to-sample task, the impact of clear or ambiguous go versus clear no-go signals on the post-imperative negative variation (PINV) was examined in 11 patients with a chronic schizophrenic disorder (DSM-III-R) and in a control group of 13 healthy subjects matched to the patient sample by age, sex, and education. Size and spatial position of a visual S2 had to be matched to one of two visual patterns in the S1 presented 4 s earlier. In 96 trials, the S2 was identical in size with one of the two patterns of S1 (clear matching). These trials varied pseudorandomly, with 60 trials in which the S2 was of intermediate size. On a randomly interspersed additional 48 trials, an S2 differing in color and shape signaled no-go. The electroencephalogram was recorded from Fz, Cz, Pz, F3, F4, C3, C4, P3, and P4. Although groups did not differ in contingent negative variation amplitude, the PINV was generally more pronounced in patients than in controls. In both groups, ambiguity of the to-be-matched S2 produced larger PINV amplitudes; the no-go signal elicited only a small PINV. Differential effects of ambiguity and no-go on PINV amplitude and its scalp distribution suggest that "performance" and "action" uncertainty contribute to PINV generation and that thresholds for both effects are reduced in schizophrenics.

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Reducing a feature vector to an optimized dimensionality is a common problem in biomedical signal analysis. This analysis retrieves the characteristics of the time series and its associated measures with an adequate methodology followed by an appropriate statistical assessment of these measures (e.g., spectral power or fractal dimension). As a step towards such a statistical assessment, we present a data resampling approach. The techniques allow estimating σ 2(F), that is, the variance of an F-value from variance analysis. Three test statistics are derived from the so-called F-ratio σ 2(F)/F 2. A Bayesian formalism assigns weights to hypotheses and their corresponding measures considered (hypothesis weighting). This leads to complete, partial, or noninclusion of these measures into an optimized feature vector. We thus distinguished the EEG of healthy probands from the EEG of patients diagnosed as schizophrenic. A reliable discriminance performance of 81% based on Taken's χ, α-, and δ-power was found.