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.
References
Aurup GMM (2011). User preference extraction from bio-signals: an experimental study (Doctoral dissertation, Concordia University)
Beam W, Borckardt JJ, Reeves ST, George MS (2009) An efficient and accurate new method for locating the F3 position for prefrontal TMS applications. Brain Stimul 2(1):50–54
Brown S, Dissanayake E (2009) The arts are more than aesthetics: Neuroaesthetics as narrow aesthetics. Neuroaesthetics 43–57
Brown L, Grundlehner B, Penders J. (2011) Towards wireless emotional valence detection from EEG. In Engineering in Medicine and Biology Society EMBC, 2011 annual international conference of the IEEE pp. 2188–2191 Augest 2011
Brown C, Randolph AB, Burkhalter JN (2012) The story of taste: using EEGs and self-reports to understand consumer choice. Kennesaw J Undergrad Res 2(1):5
Cela-Conde CJ, Marty G, Maestú F, Ortiz T, Munar E, Fernández A, Roca M, Rosselló J, Quesney F (2004) Activation of the prefrontal cortex in the human visual aesthetic perception. Proc Natl Acad Sci USA 101(16):6321–6325
Cottereau BR, McKee SP, Norcia AM (2014) Dynamics and cortical distribution of neural responses to 2D and 3D motion in human. J Neurophysiol 111(3):533–543
Craig A, Tran Y, Wijesuriya N, Nguyen H (2012) Regional brain wave activity changes associated with fatigue. Psychophysiology 49(4):574–582
Cupchik GC (1995) Emotion in aesthetics: reactive and reflective models. Poetics 23(1):177–188
Garcia D (2010) Robust smoothing of gridded data in one and higher dimensions with missing values. Comput Stat Data Anal 54(4):1167–1178
Georgieva SS, Todd JT, Peeters R, Orban GA (2008) The extraction of 3D shape from texture and shading in the human brain. Cereb Cortex 18(10):2416–2438
Geuze E, Vermetten E, Ruf M, de Kloet CS, Westenberg HG (2008) Neural correlates of associative learning and memory in veterans with posttraumatic stress disorder. J Psychiatr Res 42(8):659–669
Gielis J (2003) A generic geometric transformation that unifies a wide range of natural and abstract shapes. Am J Bot 90(3):333–338
Grimm S, Beck J, Schuepbach D, Hell D, Boesiger P, Bermpohl F, Niehaus L, Boeker H, Northoff G (2008) Imbalance between left and right dorsolateral prefrontal cortex in major depression is linked to negative emotional judgment: an fMRI study in severe major depressive disorder. Biol Psychiatry 63(4):369–376
Gunn SR (1998) Support vector machines for classification and regression. Technical report, University of Southampton, Department of electrical and computer science
Hadjidimitriou SK, Hadjileontiadis LJ (2012) Toward an EEG-based recognition of music liking using time-frequency analysis. Biomed Eng IEEE Trans 59(12):3498–3510
Hadjidimitriou SK, Hadjileontiadis LJ (2013) EEG-Based Classification of Music Appraisal Responses Using Time-Frequency Analysis and Familiarity Ratings. Affect Comput IEEE Trans 4(2):161–172
Jackson J, Goutagny R, Williams S (2011) Fast and slow gamma rhythms are intrinsically and independently generated in the subiculum. J Neurosci 31(34):12104–12117
Jeurissen D, Sack AT, Roebroeck A, Russ BE, Pascual-Leone A (2014) TMS affects moral judgment, showing the role of DLPFC and TPJ in cognitive and emotional processing. Front Neurosci 8:18. doi:10.3389/fnins.2014.00018
Khushaba RN, Kodagoda S, Dissanayake G, Greenacre L, Burke S, Louviere J (2012). A neuroscientific approach to choice modeling: Electroencephalogram (EEG) and user preferences. In Neural Networks (IJCNN), The 2012 international joint conference on pp. 1–8 IEEE June 2012
Kim Y, Kang K, Lee H, Bae C (2015) Preference measurement using user response electroencephalogram. In: Computer science and its applications. Springer Berlin, Heidelberg, pp 1315–1324
Konecni VJ (1978) Determinants of aesthetic preference and effects of exposure to aesthetic stimuli: social emotional, and cognitive factors. Prog Exp Personal Res 9:149–197
Li M, Lu BL (2009) Emotion classification based on gamma-band EEG. Engineering in Medicine and Biology Society: annual international conference of the IEEE: EMBC. 1223–1226
Liu Y, Sourina O, Nguyen MK (2010). Real-time EEG-based human emotion recognition and visualization. In Cyberworlds (CW), 2010 International Conference on pp. 262–269 IEEE October 2010
Mikutta C, Altorfer A, Strik W, Koenig T (2012) Emotions, arousal, and frontal alpha rhythm asymmetry during Beethoven’s 5th symphony. Brain Topogr 25(4):423–430
Moon J, Kim Y, Lee H, Bae C, Yoon WC (2013) Extraction of user preference for video stimuli using EEG-based user responses. ETRI J 35(6):1105–1114
Murugappan M, Murugappan S, Gerard C (2014). Wireless EEG signals based neuromarketing system using Fast Fourier Transform (FFT). InSignal Processing and its Applications (CSPA), 2014 IEEE 10th International Colloquium on pp. 25–30 IEEE March 2014
Nadal M, Munar E, Capó MÀ, Rossello J, Cela-Conde CJ (2008) Towards a framework for the study of the neural correlates of aesthetic preference. Spat Vis 21(3):379–396
Pakhomov A, Sudin N (2013) Thermodynamic view on decision-making process: emotions as a potential power vector of realization of the choice. Cogn Neurodyn 7(6):449–463
Palmer SE, Schloss KB, Sammartino J (2013) Visual aesthetics and human preference. Annu Rev Psychol 64:77–107
Paradis AL, Cornilleau-Peres V, Droulez J, Van De Moortele PF, Lobel E, Berthoz A, Le Bihan D, Poline JB (2000) Visual perception of motion and 3-D structure from motion: an fMRI study. Cereb Cortex 10(8):772–783
Pochon JB, Levy R, Poline JB, Crozier S, Lehéricy S, Pillon B, Dubois B (2001) The role of dorsolateral prefrontal cortex in the preparation of forthcoming actions: an fMRI study. Cereb Cortex 11(3):260–266
Prinz J (2007). Emotion and aesthetic value. In American philosophical association Pacific meeting Vol. 15
Schmidt LA, Trainor LJ (2001) Frontal brain electrical activity (EEG) distinguishes valence and intensity of musical emotions. Cogn Emot 15(4):487–500
Shackleton VJ (1981) Boredom and repetitive work: a review. Pers Rev 10(4):30–36
Shalbaf R, Behnam H, Moghadam HJ (2014) Monitoring depth of anesthesia using combination of EEG measure and hemodynamic variables. Cogn Neurodyn 9(1):41–51
Shephard RN, Metzler J (1971) Mental rotation of three-dimensional objects. Science 171:701–703
Song Y, Huang J, Zhou D, Zha H, Giles CL (2007) Iknn: Informative k-nearest neighbor pattern classification. In Knowledge Discovery in Databases: PKDD 2007 pp. 248–264 Springer Berlin, Heidelberg
Thorpe S, Fize D, Marlot C (1996) Speed of processing in the human visual system. Nature 381(6582):520–522
Todd JT (2004) The visual perception of 3D shape. Trends in cognitive sciences 8(3):115–121
Vuong QC, Tarr MJ (2004) Rotation direction affects object recognition. Vision Res 44(14):1717–1730
Wang XW, Nie D, Lu BL (2011) EEG-based emotion recognition using frequency domain features and support vector machines. Neural Inf Process 7062:734–743
Wang H, Li Y, Long J, Yu T, Gu Z (2014) An asynchronous wheelchair control by hybrid EEG–EOG brain–computer interface. Cogn Neurodyn 8(5):399–409
Yılmaz B, Korkmaz S, Arslan DB, Güngör E, Asyalı MH (2014) Like/dislike analysis using EEG: determination of most discriminative channels and frequencies. Comput Methods Progr Biomed 113(2):705–713
Zajonc RB (2001) Mere exposure: a gateway to the subliminal. Curr Dir Psychol Sci 10(6):224–228
Acknowledgments
This project is supported through the research Grant Ref: FRGS/2/2013/ICT02/UMS/02/1 from the Ministry of Education, Malaysia.
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Chew, L.H., Teo, J. & Mountstephens, J. Aesthetic preference recognition of 3D shapes using EEG. Cogn Neurodyn 10, 165–173 (2016). https://doi.org/10.1007/s11571-015-9363-z
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DOI: https://doi.org/10.1007/s11571-015-9363-z