Design Creativity: EEG Dataset in Loosely Controlled Modified TTCT-F Creativity Experiments

Published: 24 October 2023| Version 1 | DOI: 10.17632/24yp3xp58b.1
Morteza Zangeneh Soroush,
, Wenjun Jia,


"Creativity Dataset" includes electroencephalography (EEG) signals recorded while participants performed creativity tasks. The participants in this study consisted of 28 graduate students. All participants were right-handed and had normal or corrected vision, reported good mental health, and had no history of medical or psychiatric issues or treatment. The EEG cap was positioned according to the 10-10 international standard system. EEG signals were recorded using a 64-channel BrainVision actiCHamp and sampled at 500 Hz. The creativity experiment followed a modified figural Torrance Test of Creative Thinking (TTCT-F) test. Each creativity experiment included three incomplete sketches shown to the participants. For each sketch, participants were asked to go through three creativity tasks including idea generation, idea evolution, and idea evaluation. The idea generation section instructed participants to intuitively complete a sketch based on their initial perception of the image. In idea evolution (IDE), volunteers were tasked with creating a drawing that significantly diverged from the previous sketch. In idea evaluation (IDR), participants assessed the difficulty of thinking and drawing in the idea generation and evolution sections. All the tasks were self-paced and open-ended, lasting up to three minutes. Rest periods (RST1 and RST2) of three minutes were included at the beginning and end of each experiment. The dataset is structured with 28 (.mat) files corresponding to the 28 participants. Each participant's file contains three trials and in each trial, participants engaged in three creativity tasks: idea generation (IDG), idea evolution (IDE), and idea evaluation (IDR). We used [dataset name]_[participant number]_[trial number]_[creativity task] to name the data records. Dataset _name is “Creativity” for all the recordings. Trial number can be 1,2, or 3 as there were three creativity problems (trials). Creativity task can be IDG, or IDE, or IDR. Rest periods are named RST1 and RST2. The EEG signals are represented in 2D matrices, where rows correspond to the number of channels (63 in this dataset, as one reference channel (Cz) was removed during preprocessing), and columns represent the length of the EEG signals. EEG signals were preprocessed using the Harvard Automated Processing Pipeline for Electroencephalography (HAPPE). This involved a bandpass filter (1 to 50 Hz), identification and isolation of globally bad EEG channels, removal of artifacts through independent components analysis (ICA) and wavelet transform, detection and isolation of local bad channels within 2-second segmented epochs, and interpolation of bad channels. The EEG data was then re-referenced to an average reference. The EEG signals were segmented by aligning them with video recordings of the experiments. The preprocessed and segmented EEG data were stored in the creativity dataset. To ensure participant privacy, we omitted videos that may reveal their identity.



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