Neuro-AI Decision Prediction

Abstract

Emotions are a primary factor in determining an individual's mental health and play a significant role in their daily life. In today's evolving digital age, apps are becoming more integrated into daily routines. Understanding how these applications affect emotions is becoming increasingly important for the field of neuromarketing. Our project aims to explore in detail the dynamic relationship shared between apps and human emotions. The goal is to analyze how human moods during app usage influence decision-making, providing a comprehensive understanding of this interaction. From social media platforms to productivity tools and entertainment applications, apps offer a unique window into the human experience. Users spend significant time and emotional energy interacting with these applications. The emotions experienced while using an ever-increasing number of apps have profound effects on their mental states and satisfaction. Our study aims to delve into the structure between human emotions and digital interfaces, uncovering the complexities of this relationship. By examining the emotional responses elicited by various moods and applications, the study seeks to gain valuable insights into user experience. Ultimately, our project aims to illuminate the relationship between human emotions and applications, offering a comprehensive analysis that reflects the complexity of our digital world and leading to more empathetic and user-centered digital designs.

Keywords

• human-computer interaction
• user experience optimization
• emotion-based application interaction
• artificial intelligence in neuromarketing

References

1. [1] Adhami, M. (2013). Using neuromarketing to discover how we really feel about apps. International journal of mobile marketing, 8(1), 95-103.
2. [2] Khurana, Vaishali & Gahalawat, Monika & Kumar, Pradeep & Roy, Partha & Dogra, Debi & Scheme, Erik & Soleymani, Mohammad. (2021). A Survey on Neuromarketing using EEG Signals. IEEE Transactions on Cognitive and Developmental Systems. PP. 1-1. 10.1109/TCDS.2021.3065200.
3. [3] D. S. Moschona, "An Affective Service based on Multi-Modal Emotion Recognition, using EEG enabled Emotion Tracking and Speech Emotion Recognition," 2020 IEEE International Conference on Consumer Electronics - Asia (ICCE-Asia), Seoul, Korea (South), 2020, pp. 1-3, doi: 10.1109/ICCE-Asia49877.2020.9277291. keywords: {Speech recognition;Electroencephalography;Emotion recognition;Brain modeling;Databases;Feature extraction;Computational modeling;Emotion recognition;Emotion detection;Electroencephalography (EEG);EEG enabled Emotion Tracking;Brain waves;Brain-Computer Interface (BCI);Speech Recognition;Speech Emotion Recognition (SER);Multi-Modal Emotion Recognition;Human-Computer Interaction (HCI);Affective Service;Affective Computing}, https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9277291
4. [4] Golnar-Nik, P., Farashi, S., & Safari, M. S. (2019). The application of EEG power for the prediction and interpretation of consumer decision-making: A neuromarketing study. Physiology & behavior, 207, 90-98.
5. [5] Golnar-Nik, P., Farashi, S., & Safari, M. S. (2019). The application of EEG power for the prediction and interpretation of consumer decision-making: A neuromarketing study. Physiology & behavior, 207, 90-98.
6. [6] Understanding consumer physiological and emotional responses to food products using electroencephalography (EEG)
7. [7] How to win an award for effective advertising?, https://www.neurensics.com/en/case-tele2-how-to-win-an-award-for-effective-advertising
8. [8] Jamal, Suhaima, et al. "Integration of EEG and eye tracking technology: a systematic review." SoutheastCon 2023 (2023): 209-216.

9. [9] Ouzir, M., Lamrani, H. C., Bradley, R. L., & El Moudden, I. (2024). Neuromarketing and decision-making: Classification of consumer preferences based on changes analysis in the EEG signal of brain regions. Biomedical Signal Processing and Control, 87, 105469.
10. [10] Bano, Lorela, "LSTM-Based Model For Human Brain Decisions Using EEG Signals Analysis" (2021). Electronic Theses and Dissertations. 2291. https://digitalcommons.georgiasouthern.edu/etd/2291
11. [11] Chen Z, Wang Y, Song Z. Classification of Motor Imagery Electroencephalography Signals Based on Image Processing Method. Sensors. 2021; 21(14):4646. https://doi.org/10.3390/s21144646
12. [12] Y. Pamungkas, A. D. Wibawa and Y. Rais, "Classification of Emotions (Positive-Negative) Based on EEG Statistical Features using RNN, LSTM, and Bi-LSTM Algorithms," 2022 2nd International Seminar on Machine Learning, Optimization, and Data Science (ISMODE), Jakarta, Indonesia, 2022, pp. 275-280, doi: 10.1109/ISMODE56940.2022.10180969. keywords: {Seminars;Emotion recognition;Feature extraction;Market research;Electroencephalography;Recording;Data mining;EEG Emotion Recognition;EEG Extraction Features;RNN;LSTM;Bi-LSTM}, https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10180969
13. [13] How to win an award for effective advertising?, https://www.neurensics.com/en/case-tele2-how-to-win-an-award-for-effective-advertising
14. [14] Telpaz, A., Webb, R., & Levy, D. J. (2015). Using EEG to predict consumers’ future choices. Journal of Marketing Research, 52(4), 511–529. https://doi.org/10.1509/jmr.13.0564