Wavelet sub band entropy based feature extraction method for BCI

The study and analysis of the electrical activity of the brain is valuable in understanding the human mental state, intentions and will. This aids the development of Brain Computer Interface (BCI), facilitating communication between the human brain and computer, by converting the brain waves (EEG waves -Electroencephalography) into control signals. These control signals can then be used to trigger an external device, thereby enabling a seamless communication with the intelligent system. It unfolds various avenues for research and further applications in the realm of prosthetic device control, development of thought controlled intelligent systems and other complex interfaces. This will also be an aid to persons with disabilities or various other amputations. In this work, a novel feature extraction algorithm is proposed for extracting event related potentials from the EEG signals using wavelet sub band entropy which can be used in BCI applications. An attention index is defined which gives a measure of the amount of concentration or attention the subject has, upon focussing on a particular event or thought. (C) 2015 The Authors. Published by Elsevier B.V.