Augmented Iterations: Integrating Neural Activity in Evolutionary Computation for Design

Abstract : The principle of Augmented Iterations is to create shapes of progressively higher complexity, thanks to a fast neuronal selection of shapes among several possible evolving designs. Such a process is made possible by the use of a brain signal known as P300, which appears when a user perceives a rare and relevant stimulus and can be used for intricate pattern recognition and human computation systems. We aim at using this P300 signal to identify the (re)cognition of shapes or designs that a user finds almost instantaneously relevant and noticeable, when exposed to a rapid visual flow of variations of such shapes or designs. Using evolutionary algorithms, the shapes identified as those triggering a P300 in the user's EEG signals is selected and combined to give rise to geometrical aggregations of a higher complexity. These new shapes replace the previous ones in the rapid flow of variations presented to the user, hence iterating the evolutionary design.
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Contributeur : Fabien Lotte <>
Soumis le : mercredi 10 juillet 2013 - 13:06:17
Dernière modification le : jeudi 11 janvier 2018 - 06:24:06
Document(s) archivé(s) le : vendredi 11 octobre 2013 - 04:23:12

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  • HAL Id : hal-00843050, version 1

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Pierre Cutellic, Fabien Lotte. Augmented Iterations: Integrating Neural Activity in Evolutionary Computation for Design. eCAADe 2013, Sep 2013, Delft, Netherlands. 2013. 〈hal-00843050〉

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