Pierre Cutellic

An Inverse Modeling Method to Estimate Uncertain Spatial Configurations From 2D Information and Time-Based Visual Discriminations

CAADRIA proceedings

An inverse modeling method to estimate undertain spatial configurations from 2d information and time-based visual discriminations

arXiv (Cornell University), Feb 21, 2023

A Study Protocol for Randomized Trials on Visual ERP for multiclass discrimination in CAAD applications

Background: A great part of brain-computer interfaces (BCI’s) research has so far, been focusing ... more Background: A great part of brain-computer interfaces (BCI’s) research has so far, been focusing on interactions using exogenous responses under selective attention with synchronous and reactive methods for clear relation with controlled stimuli and the widespread applicability of the methods. Over the two past decades, event-related potentials (ERP’s) have become more and more investigated for a broader community of researchers due to the relatively little amount of training necessary for a system to perform and their detection across diverse modalities of acquisition to correlate with the sensory discrimination of dedicated stimuli. The proposed study aims to observe the detection of ERP’s components and correlated neuralphenomena under the visual presentation of complex stimuli and devise processing methods that would generalize their classification for applications in computer-aided architectural design (CAAD), where visual complexity becomes an intrinsic feature of the tasks.Me...

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Growing Shapes with a Generalised Model from Neural Correlates of Visual Discrimination

This paper focuses on the application of visual Event-Related Potentials (ERP) in better generali... more This paper focuses on the application of visual Event-Related Potentials (ERP) in better generalisations for design and architectural modelling. It makes use of previously built techniques and trained models on EEG signals of a singular individual and observes the robustness of advanced classification models to initiate the development of presentation and classification techniques for enriched visual environments by developing an iterative and generative design process of growing shapes. The pursued interest is to observe if visual ERP as correlates of visual discrimination can hold in structurally similar, but semantically different, experiments and support the discrimination of meaningful design solutions. Following bayesian terms, we will coin this endeavour a Design Belief and elaborate a method to explore and exploit such features decoded from human visual cognition.

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Towards encoding shape features with visual event-related potential based brain–computer interface for generative design

International Journal of Architectural Computing, 2019

This article will focus on abstracting and generalising a well-studied paradigm in visual, event-... more This article will focus on abstracting and generalising a well-studied paradigm in visual, event-related potential based brain–computer interfaces, for the spelling of characters forming words, into the visually encoded discrimination of shape features forming design aggregates. After identifying typical technologies in neuroscience and neuropsychology of high interest for integrating fast cognitive responses into generative design and proposing the machine learning model of an ensemble of linear classifiers in order to tackle the challenging features that electroencephalography data carry, it will present experiments in encoding shape features for generative models by a mechanism of visual context updating and the computational implementation of vision as inverse graphics, to suggest that discriminative neural phenomena of event-related potentials such as P300 may be used in a visual articulation strategy for modelling in generative design.

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Aggregation of Polyhedron Modules on a Freeform Surface

This paper presents the ongoing research to obtain an aggregated structure consisting of trihedra... more This paper presents the ongoing research to obtain an aggregated structure consisting of trihedral polyhedral modules that approximate a given ideal design surface. The aim of this research is to develop geometric and computational design methods to generate such an assembly in order to erect spatial figurations. In this paper a naming and construction method for the elements of the modules will be proposed. A categorization into regular and irregular assemblies will also be proposed. This paper can be seen as the beginning of a more detailed ongoing investigation.

Collaborative Design and Live Interaction with Parametric Models Using UbiMash

International Journal of Architectural Computing, 2010

Due to the complexity of parametric modeling, it has been the task of only a handful of experts i... more Due to the complexity of parametric modeling, it has been the task of only a handful of experts in the design team to develop, build and modify parametric models. The complexity of parametric models increases exponentially with the number of design aspects it incorporates. The ambiguity of parametric models towards the modeled design intent explains that modifying the model is often isolated as an individual exercise. Developments in physical and ubiquitous computing, however, allow for collaboration and interaction with parametric models in other ways. Communication, coordination, and interaction between parametric models and the physical and social environment are the context of this paper. The paper describes some of the projects that were outcomes of the SmartGeometry 2010 workshops. These projects are dealing with mass collaboration using Twitter, tangible interfaces, parametric design and construction coordination and geometrical interpretation of datasets.

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Machine Learning from Las Vegas

Volume!, 2016

This text describes a series of simultaneous events both in Computer Science and Architecture in ... more

UCHRON - An Event-Based Generative Design Software Implementing Fast Discriminative Cognitive Responses from Visual ERP BCI

This research aims at investigating BCI technologies in the broad scope of CAAD applications expl... more This research aims at investigating BCI technologies in the broad scope of CAAD applications exploiting early visual cognition in computational design. More precisely, this paper will describe the investigation of key BCI and ML components for the implementation and development of a software supporting this research : Uchron. It will be organised as follows. Firstly, it will introduce the pursued interest and contribution that visual-ERP EEG based BCI application for Generative Design may provide through a synthetic review of precedents and BCI technology. Secondly, selected BCI components will be described and a methodology will be presented to provide an appropriate framework for a CAAD software approach. This section main focus is on the processing component of the BCI. It distinguishes two key aspects of discrimination and generation in its design and proposes a new model based on GAN for modulated adversarial design. Emphasis will be made on the explicit use of inference loops ...

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Integrated Cognition for Iterative and Generative Designs

This paper describes the work in progress of a research on Neurodesign and a previously described... more This paper describes the work in progress of a research on Neurodesign and a previously described process entitled Augmented Iterations (Cutellic and Lotte 2013). Within this broader field of research and design explorations, we will focus on describing and commenting on results obtained in the neuro-selection of shapes among a continuously varying flow of visual stimuli. Eventually, these results will lead to the identification of a non-linear and non-convergent potential for the Human-Based Computation of iterative and generative designs. Le Cube d’Après 1

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Augmented Iterations: Integrating neural activity in evolutionary computation for design

The principle of Augmented Iterations is to create shapes of progressively higher complexity, tha... more 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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Le cube d"après : integrated cognition for iterative and generative designs

Reference EPFL-CONF-206940 URL: http://cumincad.scix.net/cgi-bin/works/Show?acadia14_473 Record c... more

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Le Cube d'Après

Augmented Iterations: Integrating neural activity in evolutionary computation for design

The principle of Augmented Iterations is to create shapes of progressively higher complexity, tha... more 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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Growing Shapes with a Generalised Model from Neural Correlates of Visual Discrimination

Proceedings of the 2020 DigitalFUTURES, 2020

This paper focuses on the application of visual Event-Related Potentials (ERP) in better generali... more This paper focuses on the application of visual Event-Related Potentials (ERP) in better generalisations for design and architectural modelling. It makes use of previously built techniques and trained models on EEG signals of a singular individual and observes the robustness of advanced classification models to initiate the development of presentation and classification techniques for enriched visual environments by developing an iterative and generative design process of growing shapes. The pursued interest is to observe if visual ERP as correlates of visual discrimination can hold in structurally similar, but semantically different, experiments and support the discrimination of meaningful design solutions. Following bayesian terms, we will coin this endeavour a Design Belief and elaborate a method to explore and exploit such features decoded from human visual cognition.

Download

Towards encoding shape features with visual event-related potential based brain–computer interface for generative design

International Journal of Architectural Computing, vol. 17, 1: pp. 88-102, 2019

This article will focus on abstracting and generalising a well-studied paradigm in visual, event-... more This article will focus on abstracting and generalising a well-studied paradigm in visual, event-related potential based brain–computer interfaces, for the spelling of characters forming words, into the visually encoded discrimination of shape features forming design aggregates. After identifying typical technologies in neuroscience and neuropsychology of high interest for integrating fast cognitive responses into generative design and proposing the machine learning model of an ensemble of linear classifiers in order to tackle the challenging features that electroencephalography data carry, it will present experiments in encoding shape features for generative models by a mechanism of visual context updating and the computational implementation of vision as inverse graphics, to suggest that discriminative neural phenomena of event-related potentials such as P300 may be used in a visual articulation strategy for modelling in generative design.

Machine Learning From Las Vegas

Machine Learning From Las Vegas, Sep 2016

This text describes a series of simultaneous events both in Computer Science and Architecture in ... more

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Aggregation of Polyhedron Modules on a Freeform Surface

by Pierre Cutellic, Rupert Maleczek, and Ursula Frick

This paper presents the ongoing research to obtain an aggregated structure consisting of trihedra... more This paper presents the ongoing research to obtain an aggregated structure consisting of trihedral polyhedral modules that approximate a given ideal design surface. The aim of this research is to develop geometric and computational design methods to generate such an assembly in order to erect spatial figurations. In this paper a naming and construction method for the elements of the modules will be proposed. A categorization into regular and irregular assemblies will also be proposed. This paper can be seen as the beginning of a more detailed ongoing investigation.

Download

Le Cube d'Après, Integrated Cognition for Iterative and Generative Designs

This paper describes the work in progress of a research on Neurodesign and a previously described... more This paper describes the work in progress of a research on Neurodesign and a previously described process entitled Augmented Iterations (Cutellic and Lotte 2013). Within this broader field of research and design explorations, we will focus on describing and commenting on results obtained in the neuro-selection of shapes among a continuously varying flow of visual stimuli. Eventually, these results will lead to the identification of a non-linear and non-convergent potential for the Human-Based Computation of iterative and generative designs.

Download

Augmented Iterations

The principle of Augmented Iterations is to create shapes of progressively higher complexity, tha... more 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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