The Survey of Electroencephalogram Signal Analysis with Auto Regressive Integrated Moving Average (ARIMA) Model

Abstract

This paper focuses on the extraction feature of Electroencephalogram signal feature and evaluating the fastest technique for signal extraction. It also explores an efficient way to extract the EEG signal characteristics. The retrieved features represent the different mental tasks that represent the condition of the brain. This research describes the usage of the ARIMA model for abstracting features from the EEG signals. ARIMA is a statistical analysis of the serial correlation and is therefore particularly effective in EEG signal categorization.