Comparison of Blind Source Separation Methods in Fast Somatosensory-Evoked Potential Detection

Blind source separation (BSS) is a promising method for extracting somatosensory-evoked potential (SEP). Although various BSS algorithms are available for SEP extraction, few studies have addressed the performance differences between them. In this study, we compared the performance of a number of typical BSS algorithms on SEP extraction from both computer simulations and clinical experiment. The algorithms we compared included second-order blind identification, estimation of signal parameters via rotation invariance technique, algorithm for multiple unknown signals extraction, joint approximate diagonalization of eigenmatrices, extended infomax, and fast independent component analysis. The performances of these BSS algorithms were determined by the correlation coefficients between the true and the extracted SEP signals. There were significant differences in the performances of the various BSS algorithms in a simulation study. In summary, second-order blind identification using six covariance matrix denoting SOBI6 was recommended as the most appropriate BSS method for fast SEP extraction from noisy backgrounds.

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