EEG Signals and New Independent Component Analysis Techniques
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Independent component analysis (ICA) is a popular blind source separation (BSS) technique that has proven to be promising for the analysis of EEG data. There are different estimators to developing these ICAs. This book presents new algorithms utilizing (i) Mutual Information based on B-Spline functions, (ii) a merger of ICA and Translation Invariant Wavelet Transform and (iii) the merger of g the B-Spline ICA with the Translation Invariant Wavelet Transform. In addition Unscented Kalman Filtering has been applied as it does not require any prior signal knowledge. Each algorithm has been examin...
Independent component analysis (ICA) is a popular blind source separation (BSS) technique that has proven to be promising for the analysis of EEG data. There are different estimators to developing these ICAs. This book presents new algorithms utilizing (i) Mutual Information based on B-Spline functions, (ii) a merger of ICA and Translation Invariant Wavelet Transform and (iii) the merger of g the B-Spline ICA with the Translation Invariant Wavelet Transform. In addition Unscented Kalman Filtering has been applied as it does not require any prior signal knowledge. Each algorithm has been examined and compared to ones in literature tackling the same EEG problems; results are drawn on the base of comparative tests on both synthetic and real signals.
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