Special topic section on advances in statistical signal processing for medicine

XCITING advances are emerging in the field of statistical signal processing that should be brought to the attention of the biomedical engineering community. Several algorithms have been proposed to separate multiple signal sources based solely on their statistical independence, instead of the more common spectral differences. These algorithms have the promise to lead to more accurate source modeling and more effective artifact rejection algorithms, two of the most challenging conditions faced in biomedical signal processing. This special issue presents six papers that illustrate current work in blind source separation (BSS) and independent component analysis (ICA). The first paper applies blind source separation to fetal electrocardiogram extraction, a practical example of two signals of the same frequency and model but independent sources that appear mixed by the recording conditions. The second paper compares several ICA algorithms to analyze optical imaging of cortical tissue in order to differentiate normally occurring variations (noise) with stimulus-driven variations (signal). The third paper studies the autonomic cardiac outflow by separating the effects of the cardiac sympathetic and parasympathetic systems using readily available RR and QT intervals. The fourth paper goes a step further than conventional approaches to utilize a priori information to bias the separation of periodic components (EKG) from the magnetoencephalo