What is blind source separation and what algorithms are used?

BSS recovers original source signals from mixtures without knowing mixing parameters. Model: x = A*s (observations = mixing matrix * sources). Methods: ICA (Independent Component Analysis) maximizes statistical independence using non-Gaussianity measures (kurtosis, negentropy), FastICA algorithm is efficient. PCA-based (maximizes variance, not independence). NMF (Non-negative Matrix Factorization) for non-negative sources. Applications: Cocktail party problem (audio separation), EEG artifact removal, image separation, and communications (MIMO detection). Assumptions: Sources are independent, at most one Gaussian, sufficient observations. Limitations: Order and scaling ambiguity.