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This study is intended to clarify the functional role of different ERP components as indicators of the processing of emotions. The effect of emotional connotation of words on hemispheric lateralization is also explored. Visual ERPs were recorded to unilaterally presented positive, negative, and neutral words that should be categorized according to their emotional connotation. The P2 amplitude was larger to positive than to negative words whereas P3 amplitude was larger to positive words compared with neutral ones. The slow positive wave (SPW) was influenced by words emotionality at anterior and posterior sites differently. The amplitude of the N1 component was larger in the left hemisphere to contralateral^ presented words. The P2 and P3 components were larger over the left hemisphere whereas the N3 and N4 components were larger over the right hemisphere to ipsilateral stimulation. The results support our hypotheses on the functional role of positive ERP components in the processing of an affective words connotation: the P2 wave reflects a general evaluation of emotional significance, the P3 a task-related decision, and the SPW an additional decision control in the context of the emotional experience of an individual. Neither the "right hemisphere hypothesis" nor "valence hypothesis" on lateralization of the processing of emotions were confirmed. Each hemisphere seems to exert its effect on emotion through specific hemispheric resources that are unequally allocated along the different stages of task processing and may cause alternation of hemispheric dominance.
We describe nonlinear deterministic versus stochastic methodology, their applications to EEG research and the neurophysiological background underlying both approaches. Nonlinear methods are based on the concept of attractors in phase space. This concept on the one hand incorporates the idea of an autonomous (stationary) system, on the other hand implicates the investigation of a long time evolution. It is an unresolved problem in nonlinear EEG research that nonlinear methods per se give no feedback about the stationarity aspect. Hence, we introduce a combined strategy utilizing both stochastic and nonlinear deterministic methods. We propose, in a first step to segment the EEG time series into piecewise quasi-stationary epochs by means of nonparametric change point analysis. Subsequently, nonlinear measures can be estimated with higher confidence for the segmented epochs fullfilling the stationarity condition.
The neurocognitive consequences of correct or incorrect spatial prediction in a sequential S1-S2 paradigm were assessed. Sequential dependence on previous trial outcome (valid or invalid) was assessed by late Event-Related Potentials (ERPs) and behavioural responses. Two different experiments were performed, situating the target in the vertical (Experiment 1) or in the horizontal (Experiment 2) meridian. RTs and late positivities (P3a and P3b) were recorded. ERPs showed that posterior positivity (probably a P3b) was greater in invalid-valid trials than in valid-valid trials but lower than in valid- invalid trials. However, at the frontal electrodes, late positivity (probably a P3a) only appeared in valid-invalid trials, indicating that invalid trials are analyzed as novel-like stimuli. The P3b results suggest trial-by-trial learning of the predictive value of the cue, which needs to be updated as indicated by the pattern of P3b amplitudes: valid-invalid > invalid-valid > valid-valid.
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