Comparative Study of Independent Component Analysis and Adaptive Filter for Removal Ocular Artifact from EEG Data

Mangesh G. Tuplondhe, Rajesh K. Agarwal


EEG is brain signal process technique that enables gaining the understanding of the complicated inner mechanisms of the brain and abnormal brain waves have shown to be related to specific brain disorders. The analysis of brain waves plays a vital role in identification of various brain disorders. MATLAB provides an interactive graphic computer programme (GUI) permitting users to flexibly and interactively method their high-density encephalogram knowledge set and different brain signal data totally different techniques similar to freelance element analysis (ICA) and reconciling filter. We are going to be showing totally different brain signals by scrutiny, analysing and simulating datasets that is already loaded within the MATLAB package to method the encephalogram signals. Unfortunately, graph knowledge is usually contaminated by ocular artifacts that create the analysis of neural knowledge terribly troublesome. The main focus of this analysis is that the development of a unique technique which will mechanically notice and take away eyeblink artifacts so as to facilitate analysis of graph recordings. During this project, we have a tendency to compare the adaptive filter and freelance part Analysis techniques. For this project we have a tendency to used EEGLAB MATLAB tool chest. By victimisation this tool chest we have done the simulation of our project.

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