Abstract
In this paper, a new method based on empirical mode decomposition (EMD) for discrimination of focal and non-focal electroencephalogram (EEG) signals is proposed. The EMD method decomposes the EEG signal into a set of narrow-band amplitude and frequency modulated (AM-FM) components known as intrinsic mode functions (IMFs). The IMFs obtained by EMD of EEG signals are plotted in a 3-D phase space diagram using phase space reconstruction (PSR). The average mean of Euclidean distances (AMED) and average standard deviation of Euclidean distances (ASED) are computed from 3-D phase space diagram. These features has been used as a feature in order to discriminate focal and non-focal EEG signals. The AMED and ASED measurement of IMFs has provided better discrimination performance. The class discriminating ability of these features are quantified using Kruskal-Wallis statistical test.
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