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BackgroundSome controversy remains about the potential applicability of cognitive potentials for evaluating the cerebral activity associated with cognitive capacity. A fundamental requirement is that these neurophysiological parameters show a high level of stability over time. Previous studies have shown that the reliability of diverse parameters of the P3 component (latency and amplitude) ranges between moderate and high. However, few studies have paid attention to the retest reliability of the P3 topography in groups or individuals. Considering that changes in P3 topography have been related to different pathologies and healthy aging, the main objective of this article was to evaluate in a longitudinal study (two sessions) the reliability of P3 topography in a group and at the individual level.ResultsThe correlation between sessions for P3 topography in the grand average of groups was high (r = 0.977, pConclusionsThe present study shows that P3 topography is highly reliable for group analysis (comprising the same subjects) in different sessions. The results also confirmed that retest reliability for individual P3 maps is suitable for follow-up studies for a particular subject. Moreover, P3 topography appears to be a specific marker considering that the between-subjects correlations were lower than the within-subject correlations. However, P3 topography appears more similar between subjects in the second session, demonstrating that is modulated by experience. Possible clinical applications of all these results are discussed.

This article explores the influence of personality on physiological data while driving in reaction to near crashes and risky situations using Machine Learning (ML). The objective is to improve the driving assistance systems in considering drivers’ characteristics. Methods:: Physiological and behavioral data were recorded in sixty-three healthy volunteers during risky urban situations and analyzed using 5 ML algorithms to discriminate the driver’s personality according to Big Five Inventory and STAI trait. Seven step process was performed including data pre-processing, Electrodermal Activity (EDA) time windows selection (one by one backward and forward approach comparison with a pseudo-wrapped), personality traits assessment, input algorithms parameters optimization, algorithm comparison and personality trait cluster prediction. ROC Area Under the Curve (AUC) was used to describe improvement. Results/discussion:: The pseudo-wrapped/all possibilities method comparison resulted in 8.3% on average for all personality traits and all algorithms (% of ROC AUC of backward and forward approach). The ROC AUC for the detection of the personality ranged between 0.968 to 0.974 with better detection of Openness, Agreeability and Neuroticism. Use of association between Neuroticism, Extraversion and Conscientiousness previously defined in the literature slightly improve personality detection (maximum ROC AUC of 0.961 to 0.993 for cluster). Results are discussed in terms of contribution to driving aids. Conclusion:: This study is one of the first to use machine learning techniques to detect personality traits using behavioral and physiological measures in a driving context. Additionally, it questions input parameters optimization approach, time windows selection, as well as clustering and association of personality trait for detection improvement.