Statistical Modeling and Forecasting of EEG Signal for BCI System Using ARIMA Model

Abstract

This paper presents a description and building of a statistical model of EEG signals with an ARIMA forecasting process. EEG measurement principles are explained for understanding EEG signals features and noise which is essential for building real-time Brain-Computer Interface (BCI). Different statistical modeling and forecasting methods of EEG signals are discussed.