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,
,
, Il y a deux niveaux qui sont construits par deux nombres pour le niveau facile (c'est-à-dire : 2 + 3) et trois nombres pour le niveau difficile (c'est-à-dire : 5 -4 + 2). La distinction entre les niveaux est le temps nécessaire pour compléter l'équation. Plus de chiffres nécessitent plus d'informations stockées dans la mémoire de travail, l'utilisation de la capacité cognitive requiert donc plus de temps pour accomplir la tâche. L'équation est affichée sur un moniteur placé à droite du participant. La figure 5.4 montre l'emplacement de l'écran à côté du simulateur de conduite. Bien que la taille de l'écran soit relativement grande (17 pouces), la résolution de l'affichage des stimuli est limitée à 720x450, Les participants doivent résoudre une équation mathématique afin de décider du point de jonction à choisir lorsqu'ils atteignent une intersection. Les stimuli de distraction sont des équations mathématiques d'addition et/ou de soustraction randomisées
, Une fois que le participant a atteint un panneau, l'écran passe de l'affichage du signe GPS droit à celui du signe GPS avec question mathématique. À ce moment, un marqueur est pointé pour avertir le groupe d'acquisition de données que le participant a entré un délai de distraction
, La distance de la "période d'affichage" est fixée à 160 m avant d'atteindre une intersection. Un autre marqueur sera à nouveau signalé lorsque le participant aura passé le deuxième panneau. Au total, les participants doivent traverser 10 intersections avant la fin de l'expérience
Écart-type du frein (N) de complexité (référence, facile et difficile) ,
, Les caractéristiques qui satisfont à l'hypothèse de normalité (p > 0,05) seront soumises à un test de Mauchly sur la sphéricité pour vérifier l'hypothèse d'homogénéité de la sphéricité, p.5
, Si les deux hypothèses sont satisfaites, une ANOVA à mesures répétées à sens unique sera effectuée pour déterminer s'il existe des différences statistiquement significatives entre les moyennes des 3 niveaux de complexité. Si l'hypothèse de sphéricité n'est pas respectée, le résultat de l'ANOVA à mesures répétées à sens unique sera interprété lorsque les ajustements selon Greenhouse-Geisser [174] ont été effectués sur les degrés de liberté pour la complexité
, Une ANOVA à mesure répétée à sens unique ou un test de Friedman ont été utilisés pour déterminer s'il y avait des différences significatives entre trois niveaux de complexité (référence, facile et difficile) pour 12 caractéristiques de mouvements oculaires sélectionnées. Des valeurs aberrantes ont été identifiées dans la moyenne et l'écart-type du point de fixation sur l'axe des y et ont été exclues de l'analyse. Des tests de Shapiro-Wilk ont été effectués sur ces données pour évaluer l'hypothèse de normalité (p > 0,05) et seul le groupe de référence de l'écart-type de la vitesse de pointe viole cette hypothèse
, ) = 2,00, p = 0,368. Les tests de Mauchly sur la sphéricité ont été effectués sur les autres caractéristiques et tous satisfont aux hypothèses (p > 0,05), d'où l'exécution d'une ANOVA à mesure répétée à sens unique. Les résultats ont montré qu'il y avait des différences statistiquement significatives entre les niveaux de complexité de l'emplacement moyen de la fixation sur l'axe des x (F (2, 12) = 5,258, Le test de Friedman sur l'écart type de la vitesse de pointe a montré qu'il n'y avait pas de différences significatives entre les trois niveaux de complexité, vol.2
, Le test t de l'échantillon par paires effectué sur toutes les paires a révélé qu'il y avait une différence statistiquement significative entre le niveau de référence et le niveau difficile (t (6) = 2,547, p < 0,044) ainsi que le niveau facile et difficile (t (6) = 2,632, p < 0,039) du point de fixation moyen sur l'axe des x. La paire de référence et facile de l'emplacement moyen de fixation sur l'axe des y a également montré une différence significative, t (6) = 2,803, p < 0,031. L'analyse de l'EEG a été réalisée à l'aide de MATLAB® version R2018b (The MathWorks Inc., USA). Les données EEG acquises ont été déduites pour soustraire les tendances linéaires, puis filtrées en passe-bande entre 0,1 Hz et 50 Hz. Les données de la période de distraction, de la période de conduite de référence et de la période d'ouverture des yeux (pour la ligne de base) ont été extraites, Une analyse post hoc a été effectuée pour déterminer les niveaux statistiquement uniques parmi les paires en utilisant le test t de l'échantillon par paires. Les différences de moyenne ont été calculées entre trois paires : référence vs facile, référence vs difficile et facile vs difficile. Les valeurs aberrantes identifiées dans la paire
, Figure 6.2. Étapes pour extraire la valeur de cohérence de l'ensemble de données
, Au total, 150 caractéristiques sont utilisées dans l'exploration des caractéristiques pertinentes pour discriminer la distraction cognitive visuelle de la conduite à l'aide d'un signal EEG
, La méthode de sélection de caractéristiques est l'un des outils importants de l'exploration de données qui a montré ses capacités dans de nombreuses applications telles que la sélection de gènes dans la classification des cancers
RFE a été mis en oeuvre pour la première fois par Guyon [140] dans l'application de la sélection de gènes pour la classification des cancers. Dans l'article de Guyon, un MVC linéaire a été utilisé pour produire un classement des caractéristiques en utilisant son poids. Les données d'entraînement sont utilisées pour construire une fonction discriminante, D(x), dans laquelle x est le vecteur d'entrée et les résultats prévus sont classés en fonction de la limite de décision apprise ,