Combining EMD with ICA for Extracting
Independent Sources
from Single Channel and Two-Channel Data
32nd Annual International Conference of the IEEE EMBS
B. Mijović
M. De Vos
I. Gligorijević
S. Van Huffel
Jain-De Le
OUTLINE
1
INTRODUCTION
2
METHODS
3
RESULTS
4
CONCLUSION
INTRODUCTION
 ICA
 The number of channels is larger than or equal to the number of
sources
 Undetermined ICA
 The number of channels is smaller than or equal to the number
of sources
 Single Channel ICA (SCICA)
 Wavelet-ICA (WICA)
 EMD-ICA
INTRODUCTION
 SCICA
 Drawbacks
• Assumes stationary sources
• The sources are assumed to be disjoint in the frequency domain
 WICA
 A wavelet transform is used to expand a 1D signal into 2D by
dividing it into its frequency subbands
 Wavelet transform has been used only for denoising
METHODS
 Single Channel EMD-ICA
 Signal is decomposed with EMD into a set of IMFs
 Perform the FastICA algorithm to the IMFs and derive the
corresponding mixing matrix A (y=Ax) and independent
components
 Select independent components of interest and multiply it with
mixing matrix A to back-reconstruct its appearance in the IMFs
set
 Sum over all the newly derived IMFs to reconstruct the
appearance of the source in the original signal
METHODS
 Two-channel EMD-ICA
 Perform the Complex EMD
 perform the Singular Value Decomposition (SVD)
 Merging both sets of reduced IMFs
 Applied ICA
 Reversible
RESULTS
原始混和信號
Single Channel EMD-ICA
(上)ECG artifact 訊號
(下)Cleaned EMG 訊號
RESULTS
RESULTS
T1
Single Channel EMD-ICA
Seizure event
Eye artifact
Muscle activity
RESULTS
將T1與F4作FastICA之結果
將T1與F4作Two-channel EMD-ICA之結果
CONCLUSION
This method is capable of extracting
more sources than channels recorded