# Deep Learning for EEG-Based Preference Classification in Neuromarketing
## 3 Literature Review
### Preference Classification Algorithms  
Although considerable progress has been made in connecting the brain activities to the user choice, indications that neural assessment could genuinely be beneficial for forecasting the success of marketing activities remain limited [24]. The neural assessments can significantly increase the predictive power above and beyond that of the traditional assessments. Because the neural assessments are better predictors than self-reported assessments, the capability of neuroscience methods to forecast the preferences in real-world situations has incredible consequences for marketers. The first study to address this was published in 2007, and it was concluded that the pre-decisional activation in the related brain areas could be used to forecast the consequent choices [46]. Since then, many neuromarketing studies have published similar conclusions. In recent years, it has become common practice to use multivariate methods, such as pattern classification, to predict choices. For example, a classification approach can be used to predict the out-of-sample choices from \"non-choice' neural responses to different products. The resulting models, which were founded on basic neuroscience methods, are more reliable for predicting the new states and settings compared to traditional market methods, such as focus groups and questionnaires. Moreover, these methods are more likely to be scalable, providing marketers with a deeper understanding of consumers and crucial economic outcomes [46]. Preference modeling using data-mining approaches can be classified into three general signal fields: time, frequency, and a combination of time and frequency. Time-based preference modeling exploits the discovery of the ERPs, as discussed in Section 3.2.2. Frequency-based modeling is accomplished by understanding the features gained by performing power spectrum analyses by generating delta, theta, alpha, beta, and gamma frequency bands, as explained in Section 3.2.1. In addition, different frequency-based feature extraction methods can be used; for instance, common spatial patterns (CSPs) and spectral filters were used in the preference classification for music with an SVM, and an accuracy of 74.77% and 68.22%, respectively, was obtained [47]. Fast Fourier transform (FFT) as the feature extraction method was used, and the SVM obtained an accuracy of 82.14% [48]. In another study, the researchers used the FFT with the radial SVMs for the preference classification and obtained an accuracy of 75.44% [49]. The last preference model combines time and frequency by analyzing the power spectrum at the time intervals that cover the entire duration of the post stimuli interval to assess the brain signals. Several traditional data-mining algorithms have been applied to classify preferences, and the utilization of different time-frequency analysis (TF) approaches has been considered [15; 23; 50] to detect the user preferences for music. The use of KNN led to an accuracy of 86.5% and 83.34% with different TF approaches, namely the Hilbert-Huang Spectrum (HHS) and spectrogram, respectively [15]. In their extended study, [PERSON] and [PERSON] [51], using familiar music data, managed to obtain a considerably higher accuracy of 91.0%. Another work involved the performance of the music preference classification by using the TF approaches, namely, the discrete Fourier transform with a KNN, and an accuracy rate of 97.99% was achieved. The researchers could achieve a similar accuracy result when using the quadratic discriminant analysis (QDA) at 97.39% [52]. Most researchers applied variations of standard regression analysis to their prediction models. However, numerous techniques and methods have been developed to process EEG to determine the preference state of consumers by using classification algorithms.