EEG Signals Independent Component Analysis and Time/Frequency Analysis by EEG Lab Tools
Keywords:
EEG, ICA, MEG, TFA, ITC, MAPAbstract
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.
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