  Brain-Computer Interfaces (BCIs) are used in various application scenarios
allowing direct communication between the brain and computers. Specifically,
electroencephalography (EEG) is one of the most common techniques for obtaining
evoked potentials resulting from external stimuli, as the P300 potential is
elicited from known images. The combination of Machine Learning (ML) and P300
potentials is promising for authenticating subjects since the brain waves
generated by each person when facing a particular stimulus are unique. However,
existing authentication solutions do not extensively explore P300 potentials
and fail when analyzing the most suitable processing and ML-based
classification techniques. Thus, this work proposes i) a framework for
authenticating BCI users using the P300 potential; ii) the validation of the
framework on ten subjects creating an experimental scenario employing a
non-invasive EEG-based BCI; and iii) the evaluation of the framework
performance defining two experiments (binary and multiclass ML classification)
and three testing configurations incrementally analyzing the performance of
different processing techniques and the differences between classifying with
epochs or statistical values. This framework achieved a performance close to
100\% f1-score in both experiments for the best classifier, highlighting its
effectiveness in accurately authenticating users and demonstrating the
feasibility of performing EEG-based authentication using P300 potentials.
