Decoding Memory: Harnessing Neuromarketing and Machine Learning to Unveil the Science of Advertisement Recall

This thesis explores the application of neuromarketing techniques to assess and predict unaided recall of advertisements using** electroencephalography** (EEG), galvanic skin response (GSR), and audiovisual features. Specifically, the research leverages advanced Machine Learning models, including Convolutional Neural Networks with Long Short-Term Memory (CNN+LSTM),** XGBoost**, AdaBoost, Random Forest, Generalized Mixed-Effect Models, Logistic Regressions, and** ANOVA**. By integrating neural and physiological responses with these predictive models, the study aims to provide a data-driven framework for optimizing advertisement effectiveness, enhancing our understanding of how memory and retrieval processes are influenced by marketing stimuli.