A Blind Source Separation Algorithm Based on Beamform Theory / 航天医学与医学工程

ABSTRACT

Objective To deal with blind source separation(BSS) more effectively in the field of mixed signal separations of strong and week sources.Methods According to the consistency between array signal processing model and BSS model,the real sources were estimated under linear constrains and least mean square(LMS),based on minimum output energy(MOE).EEG and evoked potential(EP) were used as strong background noise and week signal source separately in our experiment.The mixed signals were separated with the method proposed in this paper.Results The EP could be seperated from the strong noise EEG effectively.Conclusion Compared with typical BSS approaches,this new algorithm need not solve the unmixing matrix,so it runs fast,is of a little low computational complexity and can correctly estimate the weak signal source from low signal/noise(S/N) ratio.