The study looks at methods to better detection of Error-Related Potentials (ErrPs), which are brain signals that are generated when someone internalizes a random action that is performed. The study was performed by watching how ErrPs were used in four different scenarios: Interaction, Feedback, Response, and Observation. The study was performed using cross-subject classification across a variety of datasets to demonstrate the generality of the study’s ErrP detection process. Two requirements called R1 and R2 were proposed to test the efficiency of the study’s detection guidelines. Overall, the models met the standards for performance and also have a lower time complexity score, so they can be built much more quickly than the usual all signal samples model. The study’s novel strategy aids in generalization across a wide spectrum of users and types of experiment setups, which will increase incremental learning and make it easier to be implemented across many systems.