Conventional biometrics have been employed in high-security user-authentication systems for over 20 years now. However, some of these modalities face low-security issues in common practice. Brainwave-based user authentication has emerged as a promising alternative method, as it overcomes some of these drawbacks and allows for continuous user authentication. In the present study, we address the problem of individual user variability, by proposing a data-driven Electroencephalography (EEG)-based authentication method. We introduce machine learning techniques, in order to reveal the optimal classification algorithm that best fits the data of each individual user, in a fast and efficient manner. A set of 15 power spectral features (delta, theta, lower alpha, higher alpha, and alpha) is extracted from three EEG channels. The results show that our approach can reliably grant or deny access to the user (mean accuracy of 95.6%), while at the same time poses a viable option for real-time applications, as the total time of the training procedure was kept under one minute.

High frequency oscillations (HFOs) are an emerging biomarker of epilepsy. However, very few studies have investigated the functional connectivity of interictal iEEG signals in the frequency range of HFOs. Here, we study the corresponding functional networks using graph theory, and we assess their predictive value for automatic electrode classification in a cohort of 20 drug resistant patients. Coherence-based connectivity analysis was performed on the iEEG recordings, and six different local graph measures were computed in both sub-bands of the HFO frequency range (80-250 Hz and 250-500 Hz). Correlation analysis was implemented between the local graph measures and the ripple and fast ripple rates. Finally, the WEKA software was employed for training and testing different predictive models on the aforementioned local graph measures. The ripple rate was significantly correlated with five out of six local graph measures in the functional network. For fast ripples, their rate was also significantly (but negatively) correlated with most of the local metrics. The results from WEKA showed that the Logistic Regression algorithm was able to classify highly HFO-contaminated electrodes with an accuracy of 82.5 % for ripples and 75.4 % for fast ripples. Functional connectivity networks in the HFO band could represent an alternative to the direct use of distinct HFO events, while also providing important insights about hub epileptic areas that can represent possible surgical targets. Automatic electrode classification through FC-based classifiers can help bypass the burden of manual HFO annotation, providing at the same time similar amount of information about the epileptic tissue.