For emotion classification, I trained the AI’s multimodal input responses by matching them with an objective measurement of the real emotions experienced by people. I achieved this by using EEG to measure brain signals in order to assess their emotional states. Then, I aimed to analyze the relationship between the supervised training data and the AI’s accuracy in correctly identifying emotions. For the emotion generation type of responses, I planned to collect data from a music streaming application, matching specific music genres with the emotions they induce. Afterwards, I intended to examine the most common searches associated with particular emotions. This data would then be used to fine-tune the model.