Classification of SSVEP Based Brain Signals using Discrete Wavelet Transform
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2016, Vol 4, Issue 6
Abstract
Brain Computer Interface (BCI) sometimes called mind machine interface provides a non-muscular communication channel between the brain and the external device. It provides an alternate communication channel for people who suffer from severe motor disabilities. The Steady State Visual Evoked Potential (SSVEP) is a very suitable input signal of BCI system because of its high information transfer rate and short training time. The artifacts are disturbance that can occur during the signal acquisition and that can alter the analysis of the signals themselves. Detecting artifacts produced in electroencephalography data by electrical noise, eye blinks and muscle activity is a common and important problem in electroencephalography research. In this research, an algorithm classifies two different SSVEP signals, which are extracted by Discrete Wavelet Transform for further implementation in real life.
Authors and Affiliations
aranpreet Singh Talwar, Satnam Singh Matharu
Keywords
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- EP ID EP22301
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