Neuro-tourism is the application of neuroscience in tourism to improve marketing methods of the tourism industry by analyzing the brain activities of tourists. Neuro-tourism provides accurate real-time data on tourists’ conscious and unconscious emotions. Neuro-tourism uses the methods of neuromarketing such as brain–computer interface (BCI), eye-tracking, galvanic skin response, etc., to create tourism goods and services to improve tourist experience and satisfaction. Due to the novelty of neuro-tourism and the dearth of studies on this subject, this study offered a comprehensive analysis of the peer-reviewed journal publications in neuro-tourism research for the previous 12 years to detect trends in this field and provide insights for academics. We reviewed 52 articles indexed in the Web of Science (WoS) core collection database and examined them using our suggested classification schema. The results reveal a large growth in the number of published articles on neuro-tourism, demonstrating a rise in the relevance of this field. Additionally, the findings indicated a lack of integrating artificial intelligence techniques in neuro-tourism studies. We believe that the advancements in technology and research collaboration will facilitate exponential growth in this field.

Individual choices and preferences are important factors that impact decision making. Artificial intelligence can predict decisions by objectively detecting individual choices and preferences using natural language processing, computer vision, and machine learning. Brain–computer interfaces can measure emotional reactions and identify brain activity changes linked to positive or negative emotions, enabling more accurate prediction models. This research aims to build an individual choice prediction system using electroencephalography (EEG) signals from the Shanghai Jiao Tong University emotion and EEG dataset (SEED). Using EEG, we built different deep learning models, such as a convolutional neural network, long short-term memory (LSTM), and a hybrid model to predict choices driven by emotional stimuli. We also compared their performance with different classical classifiers, such as k-nearest neighbors, support vector machines, and logistic regression. We also utilized ensemble classifiers such as random forest, adaptive boosting, and extreme gradient boosting. We evaluated our proposed models and compared them with previous studies on SEED. Our proposed LSTM model achieved good results, with an accuracy of 96%.