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. 2016 Apr;10(2):165-73.
doi: 10.1007/s11571-015-9363-z. Epub 2015 Nov 4.

Aesthetic preference recognition of 3D shapes using EEG

Lin Hou Chew  1 , Jason Teo  1 , James Mountstephens  1
Affiliations

Aesthetic preference recognition of 3D shapes using EEG

Lin Hou Chew et al. Cogn Neurodyn. 2016 Apr.

Abstract

Recognition and identification of aesthetic preference is indispensable in industrial design. Humans tend to pursue products with aesthetic values and make buying decisions based on their aesthetic preferences. The existence of neuromarketing is to understand consumer responses toward marketing stimuli by using imaging techniques and recognition of physiological parameters. Numerous studies have been done to understand the relationship between human, art and aesthetics. In this paper, we present a novel preference-based measurement of user aesthetics using electroencephalogram (EEG) signals for virtual 3D shapes with motion. The 3D shapes are designed to appear like bracelets, which is generated by using the Gielis superformula. EEG signals were collected by using a medical grade device, the B-Alert X10 from advance brain monitoring, with a sampling frequency of 256 Hz and resolution of 16 bits. The signals obtained when viewing 3D bracelet shapes were decomposed into alpha, beta, theta, gamma and delta rhythm by using time-frequency analysis, then classified into two classes, namely like and dislike by using support vector machines and K-nearest neighbors (KNN) classifiers respectively. Classification accuracy of up to 80 % was obtained by using KNN with the alpha, theta and delta rhythms as the features extracted from frontal channels, Fz, F3 and F4 to classify two classes, like and dislike.

Keywords: 3-Dimensional (3D) shape preference; Aesthetic design; Brain-computer interface (BCI); Electroencephalogram (EEG); K-nearest neighbors (KNN); Neuro-aesthetics; Support vector machines (SVM).

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Figures

Fig. 1
Fig. 1
The flow structure of the data acquisition process
Fig. 2
Fig. 2
The 60 3D stimuli generated by Superformula
Fig. 3
Fig. 3
a The ABM b-alert X10 headset. b Electrode positions of ABM b-alert X10 according to international 10–20 electrode placement system
See this image and copyright information in PMC

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