To derive tests for randomness, nonlinear-independence, and stationarity, we combine surrogates with a nonlinear prediction error, a nonlinear interdependence measure, and linear variability measures, respectively. We apply these tests to intracranial electroencephalographic recordings (EEG) from patients suffering from pharmacoresistant focal-onset epilepsy. These recordings had been performed...

OBJECTIVE The non-stationary nature of EEG poses a major challenge to robust operation of brain-computer interfaces (BCIs). The objective of this paper is to propose and investigate a computational method to address non-stationarity in EEG classification. APPROACH We developed a novel dynamically weighted ensemble classification (DWEC) framework whereby an ensemble of multiple classifiers are...