Étude comparative en potentiels évoqués des données fournies par le biais d'analyses de groupes et individuelles

Abstract

Comparison between grand-average and individual analyses: an event-related potentials study. Purpose — Event-related potentials (ERPs) studies in human subjects have shown inter-individual response variations, probably linked to anatomical and functional brain disparities. The present study was conducted to compare the results obtained by a standard grand-average method and a single subject analysis of VEPs to faces. Material and method — Fifty-eight channel ERPs (analysis time: 1,024 ms) were recorded in 13 normal volunteers during gender or familiarity judgements on unknown and known faces, as well as on a control task using meaningless patterns. Data were then submitted to individual and group averages. Results and conclusion — Three activities were identified by both procedures: a P1/N1 complex, a vertex positive potential (P2 or VPP) associated with a temporal negativity, and a N2 negativity. These peaks displayed a marked inter-individual topographical variability. Regarding the outcome of statistical analyses, a certain number of differences were found: on P1, in which individual analyses revealed a strong effect of experimental conditions, while the grand-average method did not; on VPP, in which grand-average analyses suggested an interaction between experimental conditions, face familiarity and cerebral lateralization, while individual analyses did not; and on N2, in which grand-average data showed a clear lateralization effect, while individual analyses did not. A P3 component (Pz, 250 ms) was also defined in grand-average data, but could not be clearly described in individual data. Statistical analyses on this P3 component were thus only performed on group data and revealed a right lateralization and an interaction between face familiarity and experimental conditions. These findings confirmed the existence of a marked topographical variability of ERPs to face and, therefore, question the validity of grand-average studies. Moreover, these results suggest a better efficiency of individual analyses for studying short and middle-latency peaks, while grand-averages appear to be better suited for studying late components.