# Predicting Choices Driven by Emotional Stimuli Using EEG-Based Analysis and Deep Learning
## 2 Background
### Classification Algorithms  
In computer science, DL is one of the machine learning (ML) methods that is based on the artificial neural network (ANN). ANN, otherwise known as a neural network (NN), can be used for supervised ML and is one of the recommended models because it can recognize patterns and handle common problems by reflecting the behavior of the human brain consisting of its simplest architecture (perceptron). However, DL architecture, sometimes called a deep neural network (DNN), uses more than two hidden NN layers consisting of multi-layered perceptrons. DL has a strong ability to solve image recognition, speech recognition, natural language processing problems [13], image reconstruction [14; 15], and biomedical imaging [16]. It has also been effectively employed in the field of BCI for classifying EEG motor imagery signals and detecting emotions through EEG [5]. One of the main DL models is the CNN, which is derived from the visual cortex of animals. The data input to a CNN model is first divided into different neural fields to be fed into one- (ID) or two-dimensional convulsion layers [17; 18]. Another DL model is the recurrent neural network (RNN), which is characterized by its \"memory.\" It takes the information from previous inputs to influence the current input and output. While traditional DNN assumes that inputs and outputs are independent of each other, the output of the RNN depends on the sequence of prior elements [19]. The LSTM model is a subtype of the RNN. It has a feature through which it can memorize the sequence of the data. Thus, it is capable of learning long-term dependencies, which overcomes the issue of gradient disappearance. It is also useful for eliminating unused information. The LSTM network is parametrized by weight matrices from the input and the previous state for each of the gates [19]. A hybrid model combining CNN and LSTM is an approach where the strengths of both architectures are utilized in solving complex problems. Such a model offers the advantages of CNN's capability to learn spatial hierarchies and capture local features, along with LSTM's capacity to learn long-range temporal dependencies and manage sequences. The hybrid model architecture can vary depending on the specific problem being addressed. The choice between starting with CNN followed by LSTM or LSTM followed by CNN mainly depends on the task and the nature of the input data. The KNN algorithm is a simple, easy-to-implement supervised ML algorithm that can be used to solve both classification and regression problems. It works by classifying instances based on the similarity between them to locate the nearest neighbor. The new labeled sample will be put up against the benchmark data for comparison by the choice classification. Based on the class that the majority of KNNs belong to, the voting formula will decide where the new labeled sample will be allocated [10]. SVM is also among the most widely used supervised ML algorithms for both classification and regression problems. The fundamental concept behind SVM is to use a kernel transfer function to project incoming data onto a higher-dimensional feature space that is simpler to separate than the initial feature space [20]. LR is a supervised ML that has been widely employed in ML for EEG signal processing [21]. We employed LR, whose output value corresponds to the probability of belonging to a choice class. Ensemble models can combine many weak learners into a strong learner. RF is an ensemble-learning algorithm that uses a collection of random decision trees trained via the bagging method [22; 23]. It works by adding randomness to the tree training process and merging the results of various randomized trees into a singular classifier. The final result is a tree with decisional nodes and leaf nodes that indicate the choice class. In addition, two more ensemble-learning algorithms have been utilized in this study via the boosting method. We used AdaBoost, which is an adaptive enhancement algorithm for binary classification.