Title :
A tutorial on empirical mode decomposition in brain research
Author_Institution :
Dept. of Comput. Eng., Kwangwoon Univ., Seoul, South Korea
Abstract :
Brain electrical activity is often recorded via electroencephalogram (EEG) since it can be monitored using noninvasive and affordable recording equipment. When it comes to the analysis of EEG, there is a lack of signal processing technique to deal with the nonstationarity and nonlinearity of EEG signals. Here we present the data-driven algorithm, empirical mode decomposition suitable for the analysis of EEG.
Keywords :
bioelectric phenomena; electroencephalography; medical signal processing; EEG signal analysis; brain electrical activity; data-driven algorithm; electroencephalogram; empirical mode decomposition; signal processing technique; Algorithm design and analysis; Correlation; Electroencephalography; Empirical mode decomposition; Frequency modulation; Signal processing algorithms; Wavelet transforms; EEG; MEMD; empirical mode decomposition;
Conference_Titel :
Consumer Electronics (ISCE 2014), The 18th IEEE International Symposium on
Conference_Location :
JeJu Island
DOI :
10.1109/ISCE.2014.6884517
