Wavelet Based Feature Extraction Scheme of Electroencephalogram

The Electroencephalograph (EEG) signals is one of the most widely used in the bioinformatics field due to its rich information about human tasks. The Electroencephalogram is a neuronal activity that represents the electrical activity of the brain. The uses of EEG signals in the field of Brain computer Interface (BCI) have obtained a lot of interest with diverse applications ranging from medicine to entertainment. BCI is designed using EEG signals where the subjects have to think of only a single mental task. The specific features of EEG are used as input to Visual Evoked Potential (VEP) based Braincomputer Interface or self paced BCIs (SBCI) for communication and control purposes. This work proposes scheme to extract feature vectors using wavelet transform as alternative to the commonly used Discrete Fourier Transform (DFT). Brain Computer Interface is a direct connection between the brain and a computer, without using any of the brains natural output pathways. Visually-evoked Potentials extracted from the electroencephalographic activity in the visual cortex recorded from the overlying scalp. Wavelets are powerful candidates for decomposition, feature extraction, and classification of nonstationary EEG signals for BCI applications. Wavelet Transform (WT) is superior to Discrete Fourier Transform due to its high localization in time and frequency domain. The main objective of Wavelet Transform usage is to localize the artifact component.