The COVID-19 pandemic has profoundly disrupted daily life, leaving enduring challenges in its wake, particularly in the realm of mental health. The pandemic’s impact on individuals has been marked by substantial hardships and emotional distress, resulting in a spectrum of psychological strains. Consequently, there exists an imperative for research to address these mental health issues effectively. In 2013, Conor Russomanno and Joel Murphy introduced the OpenBCI, a revolutionary brain–computer interface (BCI) device characterized by its affordability, portability, and user-friendliness, representing a notable milestone in the field of brain computing. This study is dedicated to harnessing the potential of OpenBCI for the identification of adverse human emotions. The adoption of OpenBCI offers the promise of convenient stress measurement within the comfort of individuals’ homes, fostering the development of cost-effective stress monitoring tools for neurological practitioners. This research delves into the challenges encountered during the initial phases of data collection and analysis, elucidating the methodologies employed to prepossess the acquired raw data. Furthermore, the study presents a comprehensive inventory of machine learning (ML) models deemed suitable for the task, offering insights into the outcomes achieved by these models when applied to the datasets.