Blind source separation using the maximum signal fraction approach
Signal Processing
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust linear dimensionality reduction
IEEE Transactions on Visualization and Computer Graphics
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Analysis and Online Realization of the CCA Approach for Blind Source Separation
IEEE Transactions on Neural Networks
| Hi-index | 35.68 |
In this correspondence, we present a novel spatio-temporal extractor of source components, termed temporally local maximum signal fraction analysis (TLMSFA), which is used for artifact removal from biomedical signals. Different from classical maximum signal fraction analysis (MSFA) that uses only global spatial covariances to define signal-to-noise (SNR) expression, TLMSFA considers temporally local information in the covariance formulations used in the SNR modelling. The local time-dependent spatial covariances contain more information for source separation of biomedical signals that slowly change over time. TLMSFA is a temporal generalization of MSFA in the sense that MSFA can be formulated under the TLMSFA umbrella as a special instance. We show that TLMSFA is actually to approximately maximize the temporally local autocorrelation of the observations. By designing a time-dependent weighting function, TLMSFA is solved as a generalized eigenvalue problem. So, TLMSFA is computationally as competitive as MSFA. Empirical evaluation and experiments of source separation on real electrocardiogram data and artifact removal on real electroencephalogram data show the effectiveness of the proposed TLMSFA technique.