# Predicting Choices Driven by Emotional Stimuli Using EEG-Based Analysis and Deep Learning
## 1 Introduction  
The decision-making process is highly intricate and involves cognitive and emotional aspects [1]. Before making a choice, the brain evaluates various options and their potential outcomes. However, in an unpredictable world, the brain also owns the capacity to cancel impending actions. Past experiences, emotions, social context, and personal values are among factors that influence decision making [1, 2]. Neuroscientists use electroencephalography (EEG) to measure brain activity and find the neural correlates of the decision-making process. EEG can be used by researchers to examine how the brain reacts to various options and pinpoint the variables that affect decision making. EEG-based brain-computer interfaces (BCIs) can be used to measure how people react emotionally to various stimuli, such as pictures or videos. Decision-making BCIs have various benefits, including faster response times, improved correction of mistakes, and enhanced independent learning [3]. BCI can be classified as active or passive in terms of human control over the machine. The active BCI devices collect users' brain signals activated intentionally with user consciousness to provide human control over equipment using event-related potentials (ERP), for example, using collaborative BCI for better-coordinated group decisions in a Go/NoGo task using EEG [4]. Passive BCI devices, on the other hand, collect users' brain signals triggered unintentionally and without the users' consciousness to study users' emotions outputted involuntarily as a result of the effect of a surrounding environment [5]. This can be used to identify the true choices and obtain hidden information in users' brains about their actual thoughts, choices, and decisions. During the decision-making procedure, the human brain gathers and incorporates all sources of previous evidence and values to yield a choice. Based on such information, the decision maker gains an assessment of the probability of the decision being correct i.e., confidence. BCI can be used to predict the decision confidence from EEG using two classes 'confident' and 'non-confident' [6]. BCIs are able to identify alterations in brain activity linked to either positive or negative emotions, giving them insight into a person's preferences [7]. Artificial intelligence (AI) research can develop more accurate prediction models of individuals' behavior and preferences by understanding the neural mechanisms underlying decision making and emotional responses. By utilizing AI and deep learning (DL) models, it is possible to extract meaningful patterns and insights from large datasets, which can then be used to enhance services and applications in different contexts. This research aims to build an individual choice prediction system using EEG signals. The SJTU emotion and EEG dataset (SEED) is a benchmark dataset used in this research to investigate individuals' hidden information about actual choices using EEG signals [8]. EEG is a practical, flexible, and affordable technique. It provides a high temporal but low spatial resolution measurement of brain activity [9], which has been widely used for various purposes, including the analysis of reactions to emotional stimuli. In this paper, we build different DL models, such as a convolutional neural network (CNN), long short-term memory (LSTM), and a hybrid model. We also compared their performance with different classical classifiers, such as k-nearest neighbors (KNN), support vector machine (SVM), and logistic regression (LR). We also utilized ensemble classifiers, such as random forest (RF), adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost). We evaluated our proposed models and compared them with previous studies using SEED.