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A new supervised approach for decomposition of single channel signal mixtures is introduced in this paper. The performance of the traditional singular spectrum analysis algorithm is significantly improved by applying tensor decomposition instead of traditional singular value decomposition. As another contribution to this subspace analysis method, the inherent frequency diversity of the data has been...
Tensor based singular spectrum analysis (SSA) has been introduced as an extension of traditional singular value decomposition (SVD) based SSA. In the SSA decomposition stage PARAFAC tensor factorization has been employed. Using tensor factorization methods enable SSA to perform much better in nonstationary and underdetermined cases. The results of applying the proposed method to both synthetic and...
This paper introduces a single channel source separation of electroencephalograms (EEGs) data by combining singular spectrum analysis (SSA) subspace technique and empirical mode decomposition (EMD). In the case of single channel data, many conventional techniques such as independent component analysis (ICA) cannot be directly applied. SSA is a powerful tool to analyze such data. However, the corresponding...
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