from Single Channel and Two-Channel Data 32nd 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 Le

OUTLINE RESULTS 3 METHODS 2 INTRODUCTION 1 CONCLUSION 4

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

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