Conceptual Design Exploration: EEG Dataset in Open-ended Loosely Controlled Design Experiments

Published: 24 October 2023| Version 1 | DOI: 10.17632/h4rf6wzjcr.1


In this dataset, 42 graduate students, aged 24 to 39, participated in conceptual design experiments where Electroencephalogram (EEG) signals were recorded while participants were performing the design tasks. Three, eleven, and one participant were excluded from the experiment due to not finishing all the experiments, some technical errors, and recorded biosignals with poor quality, respectively. Finally, EEG signals from 27 participants (8 women) were stored in the dataset. Before participating, all individuals signed a consent form after being informed about the experiments and procedures. The EEG recording system used was the 64-channel BrainVision (Brain Vision Solutions, Montreal, Canada). Electrodes were placed according to the 10-10 international standard system over the participants' heads. EEGs were recorded throughout the experiments and later underwent preprocessing and segmentation. Each participant engaged in six design problems, each containing five open-ended, self-paced, and loosely controlled design tasks. These six design problems were designing: 1) a birthday cake (BDC), 2) a recycle bin (REB), 3) a toothbrush (TOB), 4) a wheelchair (WHC), 5) a workspace (WOS), and 6) a drinking fountain (DRF). Participants performed five design tasks within each design problem: understanding the problem (PU), idea generation (IG), rating generated ideas (RIG), evaluating ideas (IE), and rating idea evaluations (RIE). Additionally, each experiment included two 3-minute eye-closed rest periods, at the beginning and end of each session, represented by RST1 and RST2. To decrease the complexity of the whole design process, each design problem was divided into five open-ended tasks, providing structure without imposing excessive constraints. We aimed to replicate a real-world environment by allowing participants to work at their own pace, free from interruptions. It was the main and the only structure we placed in the whole experiment to help participants with the design problems. The dataset is organized in a folder containing 27 .mat files for the 27 participants. Each file contains EEGs of one participant recorded during their performance of design problems. EEGs are represented as variables in these files. These variables are systematically named using the pattern [“Design”]_[participant number]_[design problem number]_[task name], with participant numbers ranging from 1 to 27, design problem numbers from 1 to 6, and task names as PU, IG, RIG, IE, and RIE. To remove artifacts, recorded EEGs were referenced to Cz channel, which was later removed. This suggests that the dataset includes 63 channels of EEG signals. The recorded EEGs were preprocessed using EEGLAB and artifacts were removed. Subsequently, the preprocessed EEGs were then re-referenced to the average reference and downsampled to 250 Hz. Finally, the EEG signals were segmented using video recordings of the experiments. The preprocessed and segmented EEGs are stored in this dataset.



Concordia University


Neuroscience, Computer-Aided Design, Neural Basis of Higher-Order Cognition, Computational Neuroscience, Cognition, Creativity, Electroencephalography, Conceptual Design, Cognitive Neuroscience, Electroencephalogram, Brain-Computer Interface


Natural Sciences and Engineering Research Council of Canada


Natural Sciences and Engineering Research Council of Canada


Natural Sciences and Engineering Research Council of Canada

CDEPJ 485989-14