Advancing Brain State Classification through Electroencephalogram (EEG) Analysis: Exploring Diverse Models for Improved Classification

The combination of electroencephalography data and machine learning technologies provides a promising path for secure and transparent solutions to various problems, including authentication. The outcome of the current work provides an alternative to passwords and biometrics: the use of EEG as a method for identifying brain states such as stress and emotion. The data was extracted with the help of monitors from individual subjects at different stress and emotion intervals. Following that, CNN and Extreme Gradient Boosting were utilized for the classification task. As demonstrated, XGB outperforms CNN with high recall, accuracy, and precision. Visualizing the frequency components and mean values allows one to imagine how the data behaves. Current research has showcased that EEG-based authentication has great potential for a broad range of applications and points to future avenues for investigation, such as multimodal authentication systems or real-time authentication. To make the EEG robust and inclusive, a follow-up study could also include more in-depth feature studies, hyperparameter optimization, or possible demographic effects on the signals.