The Experts below are selected from a list of 2970 Experts worldwide ranked by ideXlab platform
Aurobinda Routray - One of the best experts on this subject based on the ideXlab platform.
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Localization of eye Saccadic signatures in Electrooculograms using sparse representations with data driven dictionaries
Pattern Recognition Letters, 2017Co-Authors: Suvodip Chakraborty, Anirban Dasgupta, Aurobinda RoutrayAbstract:Abstract In this paper, we propose two methods for localizing saccadic eye movement signatures from Electrooculograms (EOG). The first approach uses a sparse representation of data-driven dictionaries of saccadic movements. In this approach, we match the EOG subsequence with the Dictionary Element using distance metrics to identify the saccades. The second approach is to compare a saccadic signature template with the EOG subsequence using Dynamic Time Warping (DTW). We find that the proposed methods have advantages over one another in context specific applications. The first method is significantly faster with considerable accuracy, while the second approach is more accurate.
Kannan Ramchandran - One of the best experts on this subject based on the ideXlab platform.
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Denoising by sparse approximation: error bounds based on rate-distortion theory
EURASIP Journal on Advances in Signal Processing, 2006Co-Authors: Alyson K. Fletcher, Sundeep Rangan, Vivek K Goyal, Kannan RamchandranAbstract:If a signal x is known to have a sparse representation with respect to a frame, it can be estimated from a noise-corrupted observation y by finding the best sparse approximation to y. Removing noise in this manner depends on the frame efficiently representing the signal while it inefficiently represents the noise. The mean-squared error (MSE) of this denoising scheme and the probability that the estimate has the same sparsity pattern as the original signal are analyzed. First an MSE bound that depends on a new bound on approximating a Gaussian signal as a linear combination of Elements of an overcomplete Dictionary is given. Further analyses are for dictionaries generated randomly according to a spherically-symmetric distribution and signals expressible with single Dictionary Elements. Easily-computed approximations for the probability of selecting the correct Dictionary Element and the MSE are given. Asymptotic expressions reveal a critical input signal-to-noise ratio for signal recovery.
Suvodip Chakraborty - One of the best experts on this subject based on the ideXlab platform.
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Localization of eye Saccadic signatures in Electrooculograms using sparse representations with data driven dictionaries
Pattern Recognition Letters, 2017Co-Authors: Suvodip Chakraborty, Anirban Dasgupta, Aurobinda RoutrayAbstract:Abstract In this paper, we propose two methods for localizing saccadic eye movement signatures from Electrooculograms (EOG). The first approach uses a sparse representation of data-driven dictionaries of saccadic movements. In this approach, we match the EOG subsequence with the Dictionary Element using distance metrics to identify the saccades. The second approach is to compare a saccadic signature template with the EOG subsequence using Dynamic Time Warping (DTW). We find that the proposed methods have advantages over one another in context specific applications. The first method is significantly faster with considerable accuracy, while the second approach is more accurate.
Alyson K. Fletcher - One of the best experts on this subject based on the ideXlab platform.
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Denoising by sparse approximation: error bounds based on rate-distortion theory
EURASIP Journal on Advances in Signal Processing, 2006Co-Authors: Alyson K. Fletcher, Sundeep Rangan, Vivek K Goyal, Kannan RamchandranAbstract:If a signal x is known to have a sparse representation with respect to a frame, it can be estimated from a noise-corrupted observation y by finding the best sparse approximation to y. Removing noise in this manner depends on the frame efficiently representing the signal while it inefficiently represents the noise. The mean-squared error (MSE) of this denoising scheme and the probability that the estimate has the same sparsity pattern as the original signal are analyzed. First an MSE bound that depends on a new bound on approximating a Gaussian signal as a linear combination of Elements of an overcomplete Dictionary is given. Further analyses are for dictionaries generated randomly according to a spherically-symmetric distribution and signals expressible with single Dictionary Elements. Easily-computed approximations for the probability of selecting the correct Dictionary Element and the MSE are given. Asymptotic expressions reveal a critical input signal-to-noise ratio for signal recovery.
Anirban Dasgupta - One of the best experts on this subject based on the ideXlab platform.
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Localization of eye Saccadic signatures in Electrooculograms using sparse representations with data driven dictionaries
Pattern Recognition Letters, 2017Co-Authors: Suvodip Chakraborty, Anirban Dasgupta, Aurobinda RoutrayAbstract:Abstract In this paper, we propose two methods for localizing saccadic eye movement signatures from Electrooculograms (EOG). The first approach uses a sparse representation of data-driven dictionaries of saccadic movements. In this approach, we match the EOG subsequence with the Dictionary Element using distance metrics to identify the saccades. The second approach is to compare a saccadic signature template with the EOG subsequence using Dynamic Time Warping (DTW). We find that the proposed methods have advantages over one another in context specific applications. The first method is significantly faster with considerable accuracy, while the second approach is more accurate.