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Combining EMD with ICA for Extracting Independent Sources. from Single Channel and Two-Channel Data. B. Mijović M. De Vos I. Gligorijević S. Van Huffel. 32nd Annual International Conference of the IEEE EMBS. Jain-De Le. 3. 2. 1. 4. RESULTS. METHODS. INTRODUCTION.
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Combining EMD with ICA for Extracting Independent Sources from Single Channel and Two-Channel Data B. Mijović M. De Vos I. Gligorijević S. Van Huffel 32nd Annual International Conference of the IEEE EMBS Jain-De Le
3 2 1 4 RESULTS METHODS INTRODUCTION CONCLUSION OUTLINE
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 • Performthe Complex EMD • perform the SingularValue Decomposition (SVD) • Merging both sets of reducedIMFs • AppliedICA • Reversible
RESULTS 原始混和信號 Single Channel EMD-ICA (上)ECGartifact 訊號 (下)Cleaned EMG 訊號
RESULTS T1 Single Channel EMD-ICA Seizure event Eyeartifact 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 ofextracting more sources than channels recorded