from Single Channel and Two-Channel Data

32nd Annual International Conference of the IEEE EMBS32nd Annual International Conference of the IEEE EMBS

Combining EMD with ICA for Extracting Independent Sources

B. Mijović M. De Vos I. Gligorijević S. Van Huffel

Jain-De LeJain-De Le

OUTLINE

RESULTS3

METHODS2

INTRODUCTION1

CONCLUSION4

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

(上 )ECG artifact 訊號(下 )Cleaned EMG 訊號

Single Channel EMD-ICA

RESULTS

RESULTS

T1

Seizure event

Eye artifact

Muscle activity

Single Channel EMD-ICA

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