# Deep Learning for EEG-Based Preference Classification in Neuromarketing
## 3 Literature Review
### Predictive Features for the Preferences
#### 3.2.2 Transient Activities as Features  
In the cognitive processes associated with preferences, several research studies considered the ERP components N400, N200, and P300, each of which can be described as follows.  
1. **N400** Some researchers [10] found that the N400 component can reflect familiarity in forecasting hits in brand extension. A powerful association with well-known brand names was replicated in the case of larger N400 amplitudes, foreseeing greater consumer preference. Another brand extension study [38] reported that the N400 component is associated with the unconscious conceptual categorization of products and brands, albeit not with conscious assessments.
2. **N200** Some other researchers [36] observed that the N200 amplitude exposed a relationship between the emotional state and brand extension categories. This relationship appeared only with negative emotions and moderate brand extensions. A second study [43] suggested that N200 could indicate the product preferences, as determined by spontaneous procedures, whereas the LPP and PSW could indicate the product preferences, as determined by the conscious cognitive procedures. In a third study, the Cerebro system [27] combined the N200 mean, N200 minima, and ERSP to rank products according to customer preferences. Similarly, in a fourth study [3], the researchers used two methodologies, ERSP and ERP, to forecast the product preferences by examining theta brainwaves, N200, and FNR.
3. **P300** The consumer preferences for the expanded brand labels were clarified using greater P300 amplitudes [17]. The authors of [40] used P300 as a measure of the consumer preferences for certain product features. Other researchers [18; 39] have considered factors that influence the purchasing decisions. The authors of [18] investigated the roles of mathematical ability, gender, pricing, and discount promotions in the process of consumer purchasing using the active BCI. The authors correlated the 'buy' decisions with ERP components, such as P200 and P300. To understand the product preferences, the authors also evaluated the relative importance (mutual information) of the diverse product (i.e., cracker) characteristics involved in the decision-making process by evaluating the cognitive processing by using the EEG alpha, beta, and theta brainwaves [39]. The researchers used eye tracking for choosing the preferred product. [PERSON] et al. [45] used the same approach(EEG with eye tracking) to investigate the emotional reactions of tourism preferences by using different stimuli (words, images, and video). The authors observed that the images had higher affective responses than those of words in travel decision-making driven by the unconscious preference.  
The authors of [22] built a predictive model for consumer product choice from the EEG data. The researchers studied the roles of gender and age in the process of consumer preferences in terms of liking/disliking by using a passive BCI. Another research study [20] involved the use of an inductive research method to evaluate three successful and three unsuccessful advertisements by using a dense array EEG data. The results suggest that statistically significant ERP differences existed between the successful and unsuccessful advertisements.