Independent Component Analysis (ICA) is a computational technique used to separate a multivariate signal into additive, independent components. It is particularly useful in the analysis of complex signals like EEG and EMG, where different sources of activity can mix together, making it difficult to discern meaningful patterns. By applying ICA, one can effectively identify and isolate artifacts or noise, leading to cleaner signals for better interpretation and analysis.
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