EEG Signals Independent Component Analysis and Time/Frequency Analysis by EEG Lab Tools

Manini Monalisa Pradhan¹

1. Dept. of Electronics and Telecommunications / Utkalmani Gopobandhu Institute of Engineering, Rourkela, India.

Section:Research Paper, Product Type: Journal-Paper
Vol.11 , Issue.1 , pp.34-39, Feb-2023

Online published on Feb 28, 2023

Copyright © Manini Monalisa Pradhan . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
EEG machines supplies output graph records i.e. are analyses by expert knowledge of doctors. In this case we cannot found out the accurate component analysis of signal. So there may be some error. So we have two objectives. a) To propose time domain and time-space domain statistical based features that can be used to classify emotion from electroencephalogram signals (EEG). b) To implement suitable classification model to classify emotion from electroencephalogram signals (EEG) which gives efficient accuracy. We have also used EEG Lab tools of MATLAB programing language which is used for dealing out uninterrupted and event-related EEG, MEG and other electrophysiological data using neutral component analysis, time frequency analysis and artifacts elimination. This tools offers an interactive graphic user interface which permitting users to flexibly and interactively procedure their high quality density EEG and other dynamic brain data using independent component analysis (ICA) and time/frequency analysis (TFA).This paper presented using EEG machine signal implementation by EEG lab software.

Key-Words / Index Term :
EEG, ICA, MEG, TFA, ITC, MEG, MAP

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