IJSRD || National Conference on Inspired Learning || October 2015
Analysis of EEG using Ensemble Empirical Mode Decomposition
Author(s):
EEG, EEMD, Ensemble Empirical Mode Decomposition
Abstact:
This work provides the back ground for analysis of non-linear and non-stationary brain signals by using Ensemble Empirical Mode Decomposition. The electroencephalogram (EEG) is a representative signal which contains information about the condition of the human brain. The disorder in brain is characterized by recurrent electrical discharge of the cerebral cortex. Detection of such disorders by visual scanning of EEG signal is a time consuming task and it may be inaccurate, particularly for long recording data set. In this paper an algorithm is presented which is based on the concept of Ensemble Empirical Mode Decomposition (EEMD). The main idea is White Gaussian Noise is added to the original signal and then EMD is performed. This realization is done several times and by averaging the modes we obtain the true values of modes. Empirical Mode Decomposition is being done over an ensemble of the Gaussian white noise plus signal. Hence Mode mixing problem is being resolved by populating the time frequency space plane.
This work provides the back ground for analysis of non-linear and non-stationary brain signals by using Ensemble Empirical Mode Decomposition. The electroencephalogram (EEG) is a representative signal which contains information about the condition of the human brain. The disorder in brain is characterized by recurrent electrical discharge of the cerebral cortex. Detection of such disorders by visual scanning of EEG signal is a time consuming task and it may be inaccurate, particularly for long recording data set. In this paper an algorithm is presented which is based on the concept of Ensemble Empirical Mode Decomposition (EEMD). The main idea is White Gaussian Noise is added to the original signal and then EMD is performed. This realization is done several times and by averaging the modes we obtain the true values of modes. Empirical Mode Decomposition is being done over an ensemble of the Gaussian white noise plus signal. Hence Mode mixing problem is being resolved by populating the time frequency space plane.
