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Enhanced Source Extraction Using EMD-ICA for Single and Two-Channel Data Analysis

This study presents a novel approach combining Empirical Mode Decomposition (EMD) with Independent Component Analysis (ICA) to extract independent sources from both single and two-channel data. We discuss the limitations of traditional ICA methods, notably in scenarios where the number of sources exceeds the number of available channels. Our method involves decomposing signals into intrinsic mode functions (IMFs) using EMD, followed by application of the FastICA algorithm to derive independent components. Results demonstrate improved source extraction capabilities, showcasing its effectiveness in processing mixed signals, particularly in biomedical applications.

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Enhanced Source Extraction Using EMD-ICA for Single and Two-Channel Data Analysis

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  1. 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

  2. 3 2 1 4 RESULTS METHODS INTRODUCTION CONCLUSION OUTLINE

  3. 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

  4. 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

  5. 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

  6. METHODS • Two-channel EMD-ICA • Performthe Complex EMD • perform the SingularValue Decomposition (SVD) • Merging both sets of reducedIMFs • AppliedICA • Reversible

  7. RESULTS 原始混和信號 Single Channel EMD-ICA (上)ECGartifact 訊號 (下)Cleaned EMG 訊號

  8. RESULTS

  9. RESULTS T1 Single Channel EMD-ICA Seizure event Eyeartifact Muscle activity

  10. RESULTS 將T1與F4作FastICA之結果 將T1與F4作Two-channel EMD-ICA之結果

  11. CONCLUSION • This method is capable ofextracting more sources than channels recorded

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