Decoding Individual and Shared Experiences of Media Perception Using CNN Architectures

The brain is an incredibly complex organ capable of perceiving and interpreting a wide range of stimuli. Depending on individual brain chemistry and wiring, different people decipher the same stimuli differently, conditioned by their life experiences and environment. This study's objective is to decode how the CNN models capture and learn these differences and similarities in brain waves using three publicly available EEG datasets. While being exposed to a variety of media stimuli, each brain produces unique brain waves with some similarity to other neural signals to the same stimuli. However, to figure out whether our neural models are able to interpret and distinguish the common and unique signals correctly, we employed three widely used CNN architectures to interpret brain signals. We extracted the pre-processed versions of the EEG data and identified the dependency of time windows on feature learning for song and movie classification tasks, along with analyzing the performance of models on each dataset. While the minimum length snippet of 5 s was enough for the personalized model, the maximum length snippet of 30 s proved to be the most efficient in the case of the generalized model. The usage of a deeper architecture, i.e., DeepConvNet was found to be the best for extracting personalized and generalized features with the NMED-T and SEED datasets. However, EEGNet gave a better performance on the NMED-H dataset. Maximum accuracy of 69%, 100%, and 56% was achieved in the case of the personalized model on NMED-T, NMED-H, and SEED datasets, respectively. However, the maximum accuracies dropped to 18%, 37%, and 14% on NMED-T, NMED-H, and SEED datasets, respectively, in the generalized model. We achieved a 5% improvement over the state of the art while examining shared experiences on NMED-T. This marked the out-of-distribution generalization problem and signified the role of individual differences in media perception, thus emphasizing the development of personalized models along with generalized models with shared features at a certain level.