Detection of neuronal signatures by means of data-driven tomography
© Aguirre et al; licensee BioMed Central Ltd. 2013
Published: 8 July 2013
Time-frequency tomograms have been used for denoising and component separation of neuronal signals . Time-frequency tomograms are particularly appropriate to identify the time unfolding of the frequency features of the signals. However there are components of neuronal signals, as the neural signatures, that are not well represented by a clear spectral pattern. In this case, a new kind of tomographic transform has been recently proposed, the data-driven tomography. In particular, if in the linear combination of the tomographic operator B (µ, ν) = µt+νO, one chooses an operator O, that is specially tuned to the features of the component that one wants to extract, then, by looking for the particular values of the set (µ = cos(θ), ν = sin(θ)) where the noise effects might cancel out, we may separate the information of very small signals from large noise and also obtain reliable information on the temporal structure of the signal.
In Figure 1B the plot color of the tomogram (higher values in red) is built for 20 different values of the parameter θ at intervals π/40. We can see a set of high value coefficients concentrated in the 230 to 270 and 600 to 640 indexes suggesting the presence of the neural signature in both ranges of values. In Figure 1C the two neuronal signatures are extracted from the noisy original signal by projection for θ = 8π/40 from the higher value coefficients.
This work was supported by MINECO TIN2012-30883 and TIN-2010-19607.
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