To match the increasing demand for interactions with artificial intelligence (AI) in medicine and science, many strive to find the most efficient and accurate method to interact with AI models. Brain-computer interfaces (BCI) allow for direct communication between the human brain and the AI model by interpreting biosignals into commands and predictions. Direct brain communication with AI accelerates the development and heightens the reliability and accuracy of AI research. One of the most popular biosignals for BCI, electroencephalogram (EEG) data, has been widely used because of its cost-effectiveness and high precision time measurements. Error-related potential (ErrP) is the brain’s automatic response to errors and mistakes, which can be captured with EEG devices. Therefore, detecting ErrP signals enhances human interactions with AI and improves reliability in the medical and behavioral science fields. For instance, error detection can aid in motor neurorehabilitation by analyzing the ErrP signals of a rehabilitating patient’s intended motor movements. After establishing an ErrP classifier, it is possible to branch off to build an ErrP forecasting model, which predicts future brain signals and detects mistakes before they occur. Forecasting mistakes would enhance training and safety tenfold in domains of artificial intelligence where mistakes are catastrophic and must be avoided at all costs (i.e., self-driving car algorithms). In this project, we analyze the collected EEG brain signal data and develop predictive machine learning models to forecast possible future erroneous events. Various machine learning methods are explored and tested to relate human behaviors and brain signal patterns.