Year: 2021
Type: conference-paper
Abstract: MindSculpt enables users to generate a wide range of hybrid geometries in Grasshopper in real-time simply by thinking about those geometries. This design tool combines a non-invasive brain–computer interface (BCI) with the parametric design platform Grasshopper, creating an intuitive design workflow that shortens the latency between ideation and implementation compared to traditional computer-aided design tools based on mouse-and-keyboard paradigms. The project arises from transdisciplinary research between neuroscience and architecture, with the goal of building a cyber-human collaborative tool that is capable of leveraging the complex and fluid nature of thinking in the design process. MindSculpt applies a supervised machine-learning approach, based on the support vector machine model (SVM), to identify patterns of brain-waves that occur in EEG data when participants mentally rotate four different solid geometries. The researchers tested MindSculpt with participants who had no prior experience in design, and found that the tool was enjoyable to use and could contribute to design ideation and artistic endeavors.
Source: ACADIA quarterly
Authors: Qi Yang, Jesus G. Cruz-Garza, Saleh Kalantari
Institution: Cornell University
Language: English
Subfield: Architecture
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4387860146