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We propose a novel efficient method of blind signal extraction from multi-sensor networks when each observed signal consists of one global signal and local uncorrelated signals. Most of existing blind signal separation and extraction methods such as independent component analysis have constraints such as statistical independence, non-Gaussianity, and underdetermination, and they are not suitable for...
In this paper we present a powerful approach for noisy data reconstruction and also for data compression based on our algorithms for tensor factorization and decomposition [10], [9]. This approach has many potential applications in computational neuroscience, multi-sensory, multidimensional data analysis and text mining. Our algorithms are locally stable and work well for sufficiently sparse data...
Eye movements and blinks may produce unusual voltage changes that propagates from the eyeball through the head as volume conductor up to the scalp electrodes, generating severe electroencephalographic artifacts. Several methods are now available to correct the distortion induced by these events on the EEG, having different advantages and drawbacks. The main focus of this work is to quantify the performance...
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