1. Introduction

Our emotions are the main factor that determines an individual's sense of health and play a central role in a person's daily life. In today's digital age, applications have integrated into our daily routines and are playing a significant role in our lives. Understanding how these applications affect our emotions has become important. Our project explores in detail the dynamic relationship shared between applications and human emotions. The aim is to provide a comprehensive understanding of this complex interaction by analyzing how applications affect the emotional state of users.

 

Applications, ranging from social media platforms to productivity tools and entertainment apps, offer a unique window into the human experience. As users, we invest significant time and emotional energy in our interactions with these applications. The emotions we experience while using them can have a profound impact on our well-being and satisfaction.

 

Our study examines the structure of human emotions and digital interfaces in depth and aims to reveal the complexities of this relationship. We seek to gain valuable insight into the user experience by examining the emotional responses elicited by various applications. This research is not only technical but also deep research into the human soul in the digital environment.

 

Considering our day, we interact with many applications. We receive positive or negative effects from these applications, and these effects create an emotional outcome for us. Recognizing the impact these emotions have on our overall well-being is important to optimizing our digital experiences.

 

At its core, our project seeks to shed light on this complex dance between human emotions and applications by providing a comprehensive analysis that reflects the complexity of our digital world. Through this discovery, we hope to contribute valuable information to a broader understanding of human-computer interactions, paving the way for more empathetic and user-centered digital designs in the future.

 

1.1 Purpose and Importance of the Project

The importance of this project lies in understanding how applications affect users' emotions. It aims to reveal how applications affect users' emotional fluctuations by examining many emotions, from happiness to satisfaction, from boredom to anxiety. By implementing the EEG method or MR method, we aim to gather objective data on users' emotional states as they engage with these apps. Through in-depth analysis, we try to understand how people's decisions in these applications differ depending on their emotional states. For example, he explores how choices made by someone feeling down might contrast with choices made when in a cheerful mood. This discovery is vital to app design and user experience and sheds light on decision-making processes in various emotional states.

Our research could have wide-ranging implications.

 • Enhancing User Experiences: By identifying emotional triggers within apps, developers can tailor user experiences to promote positive emotions, leading to greater user satisfaction and retention.

 • Mental Health and Well-Being: Recognizing emotional patterns can help detect stress or negative emotional states, allowing for the development of apps that offer interventions or provide resources for users to manage their mental health.

• Personalized Content Delivery: Understanding the user's emotional state can inform the delivery of content, such as news, advertisements, or entertainment, to match the user's mood, enhancing engagement and relevance.

 

This project is an improvement project. We add a different dimension to previously similar studies with new perspectives and new experiments.

 

1.2 The Innovation (originality) of the Project

There have been previous studies on this subject, including surveys, self-reports, emotional analysis through facial recognition, and some emotion analysis studies using the EEG method. The main point we developed in our project is that we come to a conclusion by examining people's emotional state just before using the application and their emotional state while using the application. In previous studies, only the emotional state at the time of using the application was analyzed.

This study goes beyond simple emotional analysis, delving into the world of identifying  individuals' complicated emotional needs. By following this path, the goal is not only to grasp emotions but also to reveal and fulfill the complex emotional needs present in human experiences. This broad perspective aims to improve the application's efficiency in providing assistance and addressing users' emotional needs, changing it into a more complex and complete tool for the development of emotional well-being. In fact, this will allow it to indirectly improve the user experience in applications.

Our efforts to improve the user experience aim to enable applications to engage users for longer periods of time, allowing companies to improve their neuromarketing strategies. Our analysis evaluates users' interactions within the app in detail, highlighting why design matters. A user-friendly interface, when designed to incorporate not only aesthetics but also neuromarketing principles, can create a more positive emotional response and loyalty in users. These analyses can help companies make informed decisions regarding neuromarketing development by explaining that design is not only visual but also a strategic tool in terms of its effects on user psychology and behavior.

 

1.3 Technology Areas to which the Project is Related

Our project straddles multiple technology areas, primarily artificial intelligence, human-computer interaction, and neuroscience. Within these domains, there have been studies related to emotion analysis using EEG in controlled lab environments. However, the application of this technology to apps is relatively uncharted territory.

 

In the existing literature and market, there are studies and applications related to emotional analysis. These typically include sentiment analysis of social media content and face recognition systems that detect emotions from facial expressions. While these approaches provide valuable insights, they are often indirect and might not reflect a user's genuine emotional state while interacting with an application.

Our project distinguishes itself by offering real-time, non-invasive emotional analysis through EEG, enabling us to capture emotions at a deeper level during app usage. This innovation opens up a plethora of opportunities for user experience enhancement, targeted advertising, and potentially even mental health monitoring.

 

1.4 Related Academic Works

The field of neuromarketing has gained significant traction in recent years, with researchers employing advanced techniques to delve into the subconscious motivations and preferences of consumers. This burgeoning area has yielded valuable insights into how neural activity influences decision-making and behavior. Our study aligns with this research trajectory, aiming to explore the relationship between user brain images before using mobile apps and subsequent user behavior.

Specifically, we draw inspiration from four notable studies that have employed cutting-edge neuromarketing technology to investigate user interactions with mobile applications and consumer decision-making processes [1, 2, 3, 4]. The first study by Plastic Mobile and True Impact Marketing [1] leveraged EEG and eye tracking to assess users' emotional and attentional responses to mobile apps. The findings revealed that visual elements significantly impact brand perception, with rich imagery garnering more positive responses than text. This underscores the importance of user-friendly designs and optimized screen space on mobile devices. "Neuromarketing and decision-making: Classification of consumer preferences based on changes in the EEG signal of brain regions" [2] explored the convergence of neuromarketing and consumer decision-making processes. EEG was employed to analyze brain signal activity as consumers made choices, and machine learning algorithms, including K-nearest neighbors, Random Forest, Neural Network, and Gradient Boosting, were used to classify preferences and interpret EEG data. This interdisciplinary approach provides valuable insights into the neural basis of consumer behavior, allowing businesses to refine marketing strategies based on insights from consumers' subconscious responses.

The other study, "Gender Differences in Brain Activation During Consumer Decision-Making: A Magnetoencephalography Study" [3], utilized magnetoencephalography (MEG) to investigate the temporal dynamics of cortical activity during consumer decision-making in simulated shopping scenarios. The research, led by Sven Braeutigam, identified gender differences in decision-making strategies, with females showing stronger activation in the left parieto-occipital lobe and males in the right temporal lobe [3]. "Neuromarketing: A Survey of EEG-Based Approaches" [4] provides a comprehensive overview of EEG-based neuromarketing strategies. It explores the diverse range of information that can be gleaned from EEG data, including emotional responses, attentional patterns, and memory formation. The study also delves into the presentation of marketing stimuli, the impact of EEG-based strategies on consumer behavior, and the ethical considerations that arise in this context.

In the news article “How to Win an Award for Advertising,", for example, Tele2 used the neuroscience data of its customers to change the firm's marketing techniques, and they won a marketing award. Tele2's advertising success was driven by their adoption of neuroad testing techniques, combining fMRI analysis, eye tracking, and neuromarketing insights. By using fMRI, they probed deep into the subconscious to measure emotional responses to their ads, gaining crucial insights into audience engagement [5]. This was proof of the effectiveness of using neuro-insights to make predictions about customers. To capture neuroscience data, there are several techniques. One of the most effective ways is the method called EEG. The study “The application of EEG power for the prediction and interpretation of consumer decision-making: A neuromarketing study” delved into the application of EEG data in neuromarketing to predict consumer shopping behavior and interpret advertising impact. It had two primary goals: firstly, to explore EEG's potential in predicting consumer preferences by analyzing data collected while participants viewed ads for different mobile phone brands, and secondly, to understand how changes in ad content, like background color and promotions, influenced shopping decisions. EEG data analysis demonstrated an impressive accuracy rate exceeding 87% in predicting consumer decisions, such as product liking or purchasing [6]. The EEG technique can also be used to record brain activity during daily life activities, which is touched upon in the article “Chronic wireless streaming of invasive neural recordings at home for circuit discovery and adaptive stimulation.” Researchers have achieved a remarkable feat by developing technology to wirelessly record the ongoing brain activity of individuals with Parkinson's disease in the comfort of their own homes. This data is then used to fine-tune the stimulation delivered by implanted devices for deep brain stimulation (DBS) [7]. This innovation to be able to use the EEG technique wirelessly is beneficial for our project, which uses the EEG method to record the neural data that will be used to make predictions about consumer decisions.

Building upon these foundations, our study aims to establish a predictive relationship between user brain images before using mobile apps and subsequent user behavior. By employing advanced machine learning techniques, we seek to identify patterns in brain activity that correlate with specific user behaviors, such as engagement, retention, and overall satisfaction. This endeavor holds the potential to revolutionize the field of user experience design, enabling app developers to tailor their experiences to individual users' neural profiles, ultimately enhancing user satisfaction and engagement.

 

2. Technological Implementation of Neuro-AI Decision Prediction

 

2.1 Data Collection

 

In our project, we will record the brain activities of our subjects and extract meaningful data from the brain images. These data sets will cover the brain activity of subjects interacting with the application in different emotional states. We will use the EEG method and MR method, which are the latest technologies for brain imaging. We will conduct this study jointly with Maltepe University Faculty of Medicine neuroscience experts. In addition, our university hospital has facilities where we can conduct experiments.

2.2 Data Processing and Analysis

 

The data we obtain from brain images will first be pre-processed and cleaned. We will then train our program to work on this data with advanced deep learning algorithms and neural network models to interpret and make sense of the emotional states of the subjects. We will leverage popular deep learning libraries such as TensorFlow and PyTorch to perform analysis on complex data sets. Additionally, data mining and statistical analysis techniques will be applied to uncover patterns and relationships within the data and discover connections between user behavior and emotional states.

 

2.3 Artificial Intelligence Applications

 

Artificial intelligence plays a very important role in interpreting the data we obtain. Deep learning models will be trained to work on the emotional state data obtained from the subjects and will give us a conclusion by studying this emotional state-app usage experiences. These trained models will be continuously optimized to understand how emotional states affect users' experiences of app usage. In this way, we will reach the most accurate results. Machine learning algorithms, including recurrent neural networks and convolutional neural networks, will be used to capture the nuances of emotional fluctuations in response to different applications.

 

2.4. Image Processing Applications

 

We will use image processing techniques to obtain meaningful data for our project from the brain images obtained from the subjects. We will determine their emotional states from these images through image processing. In this way, we will obtain meaningful data that we can interpret. We will use libraries such as OpenCV for image processing.

2.5 Ethical Considerations

It is critical to underline that the project will carefully follow ethical rules and protect the participants' privacy and confidentiality throughout these procedures. The data will be anonymized and securely kept when informed consent is received. Furthermore, the project will be carried out with complete transparency and openness, allowing for peer review and validation of the approach used.

3. Conclusion

Finally, our study investigates the complicated relationship between human emotions and digital applications using modern neuroscience technologies such as EEG and MR. We provide a thorough knowledge of how our emotions influence the user experience and the impact of apps on individuals by assessing emotional states both before and during application engagement.

This project was prepared through the collaborative efforts of Erman Yalçın, Arda Deniz Küçükçoban, Melis Kara, Doğa Akal, and Sanem Duran under the supervision of Emre Olca, which has the potential to transform user experiences and give light on multiple industries such as mental health and application design. Our program includes critical phases such as data collecting, processing, and analysis that make use of image processing, artificial intelligence, and machine learning.