Statistical analysis of event-related potential elicited by verb-complement merge in Brazilian Portuguese

• Autor(a) principal: Cagy,M.
• Data de Publicação: 2006
• Outros Autores: Infantosi,A.F.C., França,A.I., Lemle,M.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Brazilian Journal of Medical and Biological Research
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-879X2006001100010
• topic: Event-related brain potentials; Verb-complement merges; Averaging; N400 identification

An interesting fact about language cognition is that stimulation involving incongruence in the merge operation between verb and complement has often been related to a negative event-related potential (ERP) of augmented amplitude and latency of ca. 400 ms - the N400. Using an automatic ERP latency and amplitude estimator to facilitate the recognition of waves with a low signal-to-noise ratio, the objective of the present study was to study the N400 statistically in 24 volunteers. Stimulation consisted of 80 experimental sentences (40 congruous and 40 incongruous), generated in Brazilian Portuguese, involving two distinct local verb-argument combinations (nominal object and pronominal object series). For each volunteer, the EEG was simultaneously acquired at 20 derivations, topographically localized according to the 10-20 International System. A computerized routine for automatic N400-peak marking (based on the ascendant zero-cross of the first waveform derivative) was applied to the estimated individual ERP waveform for congruous and incongruous sentences in both series for all ERP topographic derivations. Peak-to-peak N400 amplitude was significantly augmented (P < 0.05; one-sided Wilcoxon signed-rank test) due to incongruence in derivations F3, T3, C3, Cz, T5, P3, Pz, and P4 for nominal object series and in P3, Pz and P4 for pronominal object series. The results also indicated high inter-individual variability in ERP waveforms, suggesting that the usual procedure of grand averaging might not be considered a generally adequate approach. Hence, signal processing statistical techniques should be applied in neurolinguistic ERP studies allowing waveform analysis with low signal-to-noise ratio.