Locating Information Maximization Time in EEG Signals Recorded During Mental Tasks

Brain-Computer interfacing systems require online processing of electroencephalogram (EEG) signals. Therefore, high speed signal processing is of great importance in BCI systems. In this study, we suggest and calculate a time pattern for information variations within the EEG signal recorded during mental tasks. Therefore, it is possible to limit the length of the EEG signal from beginning up to the information maximization point, hence reducing the processing time. In this work Wavelet entropy is employed for information content evaluation of the EEG signal.