Discrimination of Customers Decision-Making in a Like/Dislike Shopping Activity Based on Genders: A Neuromarketing Study - Atefe Hassani; Amin Hekmatmanesh; Ali Motie Nasrabadi (IEEE Access)
The present study considers the decision making of customers in a Like/Dislike task with respect to the gender of customers. The investigation is performed by recording electroencephalography (EEG) signal from 20 subjects that stimulated by displaying images of shoes. In the algorithm, the EEG signals were denoised by using artifact subspace reconstruction and independent component analysis methods. The Wavelet technique is then applied to attain five EEG frequency bands and, subsequently linear and nonlinear features were extracted. The extracted features includes linear features, namely the power spectral density and energy of wavelet; and nonlinear features, namely the fractal dimension, entropy, and trajectory volume behavior quantifiers. The meaningfulness of the features for identifying discriminative channels as well as frequency bands is considered by means of Wilcoxon Rank Sum statistical test. The identifications of Like/Dislike conditions were then facilitated by the Support Vector Machine, Random Forest, Linear Discriminant Analysis, and K-Nearest Neighbors classifiers. Results illustrated that higher frequency bands, the combination of theta, alpha, and beta, in Fp1, Fp2, F7, F8, Cz, and Pz regions was observed for female group. The most distinctive feature and classifier for the female group was the energy of the wavelet coefficient and RF classifier, respectively, that produced the highest accuracy rate of 71.51± 5.1%. In addition, the most distinctive features for males were sample and approximate entropy, as well as the Higuchi fractal dimension that with the RF classifier produced an accuracy rate of 71.33± 14.07%. The nonlinear features investigation revealed more involved brain regions in a Like/Dislike task than the previous studies. In addition, it is revealed that the Like decision-making happens earlier than Dislike.