A new technique for blind source separation using subband subspace analysis in correlated multichannel signal environments

We investigated a new framework for the problem of blind source identification in multichannel signal processing. Inspired by a neurophysiological data environment, where an array of closely spaced recording electrodes is surrounded by multiple neural cell sources, significant spatial correlation of source signals motivated the need for an efficient technique for reliable multichannel blind source identification. In a previous work Oweiss and Anderson (see Proceedings of the 34th. Asilomar Conference on Signals, Systems and Computers, Pacific Grove, 2000) adopted a new approach for noise suppression based on thresholding an array discrete wavelet transform (ADWT) representation of the multichannel data. We extend the work of Oweiss and Anderson to identify sources from the observation mixtures. The technique relies on separating sources with highest spatial energy distribution in each frequency subband spanned by the corresponding wavelet basis. Accordingly, the best basis selection criterion we propose benefits from the additional degree of freedom offered by the space domain. The amplitude and shift invariance properties revealed by this technique make it very efficient to track spatial source variations sometimes encountered in multichannel neural recordings. Results from multichannel multiunit neural data are presented and the overall performance is evaluated